Date: (Mon) Apr 27, 2015
Data: Source: Training: https://courses.edx.org/c4x/MITx/15.071x_2/asset/tweets.csv
New:
Time period:
Based on analysis utilizing <> techniques,
Use plot.ly for interactive plots ?
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
extensions toward multiclass classification are scheduled for the next release
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/mydsutils.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
# Gather all package requirements here
#suppressPackageStartupMessages(require())
#packageVersion("snow")
#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/c4x/MITx/15.071x_2/asset/tweets.csv"
glb_newdt_url <- "<newdt_url>"
glb_is_separate_newent_dataset <- FALSE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 1 - 0.7 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_max_obs <- NULL # or any integer
glb_is_regression <- FALSE; glb_is_classification <- TRUE; glb_is_binomial <- TRUE
glb_rsp_var_raw <- "Avg"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Negative.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) {
relevel(factor(ifelse(raw <= -1, "Y", "N")), as.factor(c("Y", "N")), ref="N")
#as.factor(paste0("B", raw))
#as.factor(raw)
}
glb_map_rsp_raw_to_var(c(-2, -1, 0, 1, 2))
## [1] Y Y N N N
## Levels: N Y
glb_map_rsp_var_to_raw <- function(var) {
#as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
c("N", "Y")[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(-2, -1, 0, 1, 2)))
## [1] "Y" "Y" "N" "N" "N"
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
glb_id_vars <- NULL # or c("<id_var>")
# List transformed vars
glb_exclude_vars_as_features <- c("Tweet.fctr")
# List feats that shd be excluded due to known causation by prediction variable
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(NULL)) # or c("<col_name>")
# List output vars (useful during testing in console)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# grep(glb_rsp_var_out, names(glb_trnent_df), value=TRUE))
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_is_textual <- TRUE # vs. glb_is_numerical ???
#Sys.setlocale("LC_ALL", "C") # For english
# rpart: .rnorm messes with the models badly
# caret creates dummy vars for factor feats which messes up the tuning
# - better to feed as.numeric(<feat>.fctr) to caret
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
glb_models_lst <- list(); glb_models_df <- data.frame()
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=2, max=4, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
glb_out_pfx <- "Apple_Tweets_"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_script_tm <- proc.time()
glb_script_df <- data.frame(chunk_label="import_data",
chunk_step_major=1, chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"])
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002
1: import dataglb_entity_df <- myimport_data(url=glb_trnng_url,
comment=ifelse(!glb_is_separate_newent_dataset, "glb_entity_df", "glb_trnent_df"),
force_header=TRUE)
## [1] "Reading file ./data/tweets.csv..."
## [1] "dimensions of data in ./data/tweets.csv: 1,181 rows x 2 cols"
## Tweet
## 1 I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore
## 2 iOS 7 is so fricking smooth & beautiful!! #ThanxApple @Apple
## 3 LOVE U @APPLE
## 4 Thank you @apple, loving my new iPhone 5S!!!!! #apple #iphone5S pic.twitter.com/XmHJCU4pcb
## 5 .@apple has the best customer service. In and out with a new phone in under 10min!
## 6 @apple ear pods are AMAZING! Best sound from in-ear headphones I've ever had!
## Avg
## 1 2.0
## 2 2.0
## 3 1.8
## 4 1.8
## 5 1.8
## 6 1.8
## Tweet
## 41 I love how @apple makes it easy to transfer between Macs while I continue to work! pic.twitter.com/lga2KaXa4b
## 180 Pretty sure if I could have any voice in the world it would be @apple's own #JonyIve
## 384 @llombardo007 @Apple Will not realese 0.7.5 until after ios7 comes out(on the 18th)
## 601 #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune #IPOD GENERATION
## 858 iPhone 5C = 5 cheap... Plastic = not worth the money Metal forever @Apple... Get it right!
## 1166 freak YOU @APPLE
## Avg
## 41 1.0
## 180 0.4
## 384 0.0
## 601 0.0
## 858 -0.6
## 1166 -2.0
## Tweet
## 1176 freak u @apple
## 1177 freak @apple
## 1178 WHY CANT I freakING SEE PICTURES ON MY TL IM ANNOYED freak YOU @TWITTER @APPLE
## 1179 @APPLE YOU freakING COWS freak YOU
## 1180 @apple I hate you why is my phone not working I'm going to freak out
## 1181 @aGounalakis that's nasty! @apple is a nasty brat
## Avg
## 1176 -2
## 1177 -2
## 1178 -2
## 1179 -2
## 1180 -2
## 1181 -2
## 'data.frame': 1181 obs. of 2 variables:
## $ Tweet: chr "I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore" "iOS 7 is so fricking smooth & beautiful!! #ThanxApple @Apple" "LOVE U @APPLE" "Thank you @apple, loving my new iPhone 5S!!!!! #apple #iphone5S pic.twitter.com/XmHJCU4pcb" ...
## $ Avg : num 2 2 1.8 1.8 1.8 1.8 1.8 1.6 1.6 1.6 ...
## - attr(*, "comment")= chr "glb_entity_df"
## NULL
if (!glb_is_separate_newent_dataset) {
glb_trnent_df <- glb_entity_df; comment(glb_trnent_df) <- "glb_trnent_df"
} # else glb_entity_df is maintained as is for chunk:inspectORexplore.data
if (glb_is_separate_newent_dataset) {
glb_newent_df <- myimport_data(
url=glb_newdt_url,
comment="glb_newent_df", force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_entity_df <- rbind(glb_trnent_df, glb_newent_df); comment(glb_entity_df) <- "glb_entity_df"
} else {
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newent_df <- glb_trnent_df[sample(1:nrow(glb_trnent_df),
max(2, nrow(glb_trnent_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newent_df <- do.call("subset",
list(glb_trnent_df, parse(text=glb_split_newdata_condition)))
glb_trnent_df <- do.call("subset",
list(glb_trnent_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnent_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newent_df <- glb_trnent_df[!split, ]
glb_trnent_df <- glb_trnent_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnent_df <- glb_entity_df
comment(glb_trnent_df) <- "glb_trnent_df"
glb_newent_df <- glb_entity_df
comment(glb_newent_df) <- "glb_newent_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newent_df) <- "glb_newent_df"
myprint_df(glb_newent_df)
str(glb_newent_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnent_df)
str(glb_trnent_df)
}
}
## Loading required package: caTools
## Tweet
## 2 iOS 7 is so fricking smooth & beautiful!! #ThanxApple @Apple
## 5 .@apple has the best customer service. In and out with a new phone in under 10min!
## 8 the iPhone 5c is so beautiful <3 @Apple
## 11 I love the new iOS so much!!!!! Thnx @apple @phillydvibing
## 16 Just watched the keynote of @apple latest iPhones. I just love the #iPhone5S and #iphone5c ??? I guess, i have a christmas gift already????
## 20 Can't wait for my #orange phone upgrade in November :-) #apple iPhone 5s here I come ;-) @OrangeHelpers @apple
## Avg
## 2 2.0
## 5 1.8
## 8 1.6
## 11 1.6
## 16 1.6
## 20 1.4
## Tweet
## 171 FOLLOW @APPLE NOW
## 256 A #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune
## 474 {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune}
## 809 Waay too many people waiting for #AppleCare, in my @Apple era this would not have been acceptable #Commodity pic.twitter.com/hgzYTEx3ya
## 933 @apple #failed big time with their new iPhones. What happened to it being a "budget" phone?
## 1022 I don't like #iOS7 they're changing the look of everything @apple
## Avg
## 171 0.4
## 256 0.2
## 474 0.0
## 809 -0.4
## 933 -0.8
## 1022 -1.0
## Tweet
## 1158 I freaking hate you @apple
## 1167 freak you @apple
## 1170 @apple worst customer service ever.
## 1173 We should boycott @Apple or freakin flame them or something like how they have all this money but can't make stable chargers #freakApple
## 1174 wtf @telstra @apple why would you have pre-order for the 5c the crap phone no one wants and not the 5s i hate you plz die
## 1177 freak @apple
## Avg
## 1158 -1.8
## 1167 -2.0
## 1170 -2.0
## 1173 -2.0
## 1174 -2.0
## 1177 -2.0
## 'data.frame': 356 obs. of 2 variables:
## $ Tweet: chr "iOS 7 is so fricking smooth & beautiful!! #ThanxApple @Apple" ".@apple has the best customer service. In and out with a new phone in under 10min!" "the iPhone 5c is so beautiful <3 @Apple" "I love the new iOS so much!!!!! Thnx @apple @phillydvibing" ...
## $ Avg : num 2 1.8 1.6 1.6 1.6 1.4 1.4 1.4 1.2 1.2 ...
## - attr(*, "comment")= chr "glb_newent_df"
## Tweet
## 1 I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore
## 3 LOVE U @APPLE
## 4 Thank you @apple, loving my new iPhone 5S!!!!! #apple #iphone5S pic.twitter.com/XmHJCU4pcb
## 6 @apple ear pods are AMAZING! Best sound from in-ear headphones I've ever had!
## 7 Omg the iPhone 5S is so cool it can read your finger print to unlock your iPhone 5S and to make purchases without a passcode #Apple @Apple
## 9 #AttributeOwnership is exactly why @apple will always be #one! #apple #marketing #marketer #business #innovation #fb
## Avg
## 1 2.0
## 3 1.8
## 4 1.8
## 6 1.8
## 7 1.8
## 9 1.6
## Tweet
## 144 How @Apple Is Improving Mobile App Security http://fw.to/Qo0DqXT #Mobile #App
## 514 {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune}
## 653 Dear @Apple im sure im not the only one that types "nerd" so lets stop putting a red line under it. Thanks, Diego??
## 700 OS X 10.8.5 indirilmeye sunulmus @apple
## 816 .@Apple discontinues Cards app and printing service, recommends using #iPhoto instead. http://ht.ly/oPzms
## 934 I'm PISSED that Pages is now free @apple where is my refund for buying it! LOL
## Avg
## 144 0.4
## 514 0.0
## 653 -0.2
## 700 -0.2
## 816 -0.4
## 934 -0.8
## Tweet
## 1175 Hate you @apple
## 1176 freak u @apple
## 1178 WHY CANT I freakING SEE PICTURES ON MY TL IM ANNOYED freak YOU @TWITTER @APPLE
## 1179 @APPLE YOU freakING COWS freak YOU
## 1180 @apple I hate you why is my phone not working I'm going to freak out
## 1181 @aGounalakis that's nasty! @apple is a nasty brat
## Avg
## 1175 -2
## 1176 -2
## 1178 -2
## 1179 -2
## 1180 -2
## 1181 -2
## 'data.frame': 825 obs. of 2 variables:
## $ Tweet: chr "I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore" "LOVE U @APPLE" "Thank you @apple, loving my new iPhone 5S!!!!! #apple #iphone5S pic.twitter.com/XmHJCU4pcb" "@apple ear pods are AMAZING! Best sound from in-ear headphones I've ever had!" ...
## $ Avg : num 2 1.8 1.8 1.8 1.8 1.6 1.6 1.6 1.6 1.6 ...
## - attr(*, "comment")= chr "glb_trnent_df"
if (!is.null(glb_max_obs)) {
if (nrow(glb_trnent_df) > glb_max_obs) {
warning("glb_trnent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
org_entity_df <- glb_trnent_df
glb_trnent_df <- org_entity_df[split <-
sample.split(org_entity_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
org_entity_df <- NULL
}
# if (nrow(glb_newent_df) > glb_max_obs) {
# warning("glb_newent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
# org_newent_df <- glb_newent_df
# glb_newent_df <- org_newent_df[split <-
# sample.split(org_newent_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
# org_newent_df <- NULL
# }
}
if (nrow(glb_trnent_df) == nrow(glb_entity_df))
warning("glb_trnent_df same as glb_entity_df")
if (nrow(glb_newent_df) == nrow(glb_entity_df))
warning("glb_newent_df same as glb_entity_df")
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="cleanse_data",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002
## elapsed1 cleanse_data 2 0 0.503
2: cleanse dataglb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="inspectORexplore.data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed1 cleanse_data 2 0 0.503
## elapsed2 inspectORexplore.data 2 1 0.538
2.1: inspect/explore data#print(str(glb_trnent_df))
#View(glb_trnent_df)
# List info gathered for various columns
# <col_name>: <description>; <notes>
# Create new features that help diagnostics
# Create factors of string variables
str_vars <- sapply(1:length(names(glb_trnent_df)),
function(col) ifelse(class(glb_trnent_df[, names(glb_trnent_df)[col]]) == "character",
names(glb_trnent_df)[col], ""))
if (length(str_vars <- setdiff(str_vars[str_vars != ""],
glb_exclude_vars_as_features)) > 0) {
warning("Creating factors of string variables:", paste0(str_vars, collapse=", "))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
for (var in str_vars) {
glb_entity_df[, paste0(var, ".fctr")] <- factor(glb_entity_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_trnent_df[, paste0(var, ".fctr")] <- factor(glb_trnent_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_newent_df[, paste0(var, ".fctr")] <- factor(glb_newent_df[, var],
as.factor(unique(glb_entity_df[, var])))
}
}
## Warning: Creating factors of string variables:Tweet
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
add_new_diag_feats <- function(obs_df, ref_df=glb_entity_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>.log=log(<col.name>),
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newent_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
print(summary(obs_df))
print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_entity_df <- add_new_diag_feats(glb_entity_df)
## Loading required package: plyr
## Tweet Avg
## Length:1181 Min. :-2.0000
## Class :character 1st Qu.:-0.6000
## Mode :character Median : 0.0000
## Mean :-0.1931
## 3rd Qu.: 0.2000
## Max. : 2.0000
##
## Tweet.fctr
## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune}: 9
## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune} : 9
## C #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 8
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune #IPOD GENERATION : 7
## FOLLOW @APPLE NOW : 5
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 5
## (Other) :1138
## .rnorm
## Min. :-3.04697
## 1st Qu.:-0.71210
## Median :-0.02806
## Mean :-0.02602
## 3rd Qu.: 0.65972
## Max. : 3.30433
##
## Tweet Avg Tweet.fctr .rnorm
## 0 0 0 0
glb_trnent_df <- add_new_diag_feats(glb_trnent_df)
## Tweet Avg
## Length:825 Min. :-2.0000
## Class :character 1st Qu.:-0.6000
## Mode :character Median : 0.0000
## Mean :-0.1908
## 3rd Qu.: 0.2000
## Max. : 2.0000
##
## Tweet.fctr
## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune}: 8
## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune} : 7
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune #IPOD GENERATION : 6
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 4
## C #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 4
## @apple : 3
## (Other) :793
## .rnorm
## Min. :-3.825193
## 1st Qu.:-0.714436
## Median : 0.002213
## Mean :-0.024802
## 3rd Qu.: 0.663992
## Max. : 3.851758
##
## Tweet Avg Tweet.fctr .rnorm
## 0 0 0 0
glb_newent_df <- add_new_diag_feats(glb_newent_df)
## Tweet Avg
## Length:356 Min. :-2.0000
## Class :character 1st Qu.:-0.6000
## Mode :character Median : 0.0000
## Mean :-0.1983
## 3rd Qu.: 0.2000
## Max. : 2.0000
##
## Tweet.fctr
## C #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 4
## FOLLOW @APPLE NOW : 3
## A #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 2
## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune}: 2
## @battalalgoos @apple : 2
## iOS 7 is so fricking smooth & beautiful!! #ThanxApple @Apple : 1
## (Other) :342
## .rnorm
## Min. :-2.940898
## 1st Qu.:-0.513629
## Median :-0.004035
## Mean : 0.028237
## 3rd Qu.: 0.621616
## Max. : 2.519880
##
## Tweet Avg Tweet.fctr .rnorm
## 0 0 0 0
# Histogram of predictor in glb_trnent_df & glb_newent_df
plot_df <- rbind(cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="Training")),
cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="New")))
print(myplot_histogram(plot_df, glb_rsp_var_raw) + facet_wrap(~ .data))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
# used later in encode.retype.data chunk
glb_display_class_dstrb <- function(var) {
plot_df <- rbind(cbind(glb_trnent_df[, var, FALSE],
data.frame(.data="Training")),
cbind(glb_trnent_df[, var, FALSE],
data.frame(.data="New")))
xtab_df <- mycreate_xtab(plot_df, c(".data", var))
rownames(xtab_df) <- xtab_df$.data
xtab_df <- subset(xtab_df, select=-.data)
print(xtab_df / rowSums(xtab_df))
}
if (glb_is_classification) glb_display_class_dstrb(glb_rsp_var_raw)
## Loading required package: reshape2
## Avg.-2 Avg.-1.8 Avg.-1.6 Avg.-1.4 Avg.-1.2
## New 0.01454545 0.01818182 0.007272727 0.01939394 0.03272727
## Training 0.01454545 0.01818182 0.007272727 0.01939394 0.03272727
## Avg.-1 Avg.-0.8 Avg.-0.6 Avg.-0.4 Avg.-0.2 Avg.0
## New 0.06060606 0.06424242 0.06060606 0.07151515 0.1078788 0.2860606
## Training 0.06060606 0.06424242 0.06060606 0.07151515 0.1078788 0.2860606
## Avg.0.2 Avg.0.4 Avg.0.6 Avg.0.8 Avg.1 Avg.1.2
## New 0.0969697 0.04242424 0.03757576 0.02787879 0.02181818 0.00969697
## Training 0.0969697 0.04242424 0.03757576 0.02787879 0.02181818 0.00969697
## Avg.1.4 Avg.1.6 Avg.1.8 Avg.2
## New 0.007272727 0.007272727 0.004848485 0.001212121
## Training 0.007272727 0.007272727 0.004848485 0.001212121
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) > 0) {
id_vars_dups_df <- subset(id_vars_df <-
mycreate_tbl_df(glb_entity_df[, glb_id_vars, FALSE], glb_id_vars),
.freq > 1)
} else {
tmp_entity_df <- glb_entity_df
tmp_entity_df$.rownames <- rownames(tmp_entity_df)
id_vars_dups_df <- subset(id_vars_df <-
mycreate_tbl_df(tmp_entity_df[, ".rownames", FALSE], ".rownames"),
.freq > 1)
}
if (nrow(id_vars_dups_df) > 0) {
warning("Duplicates found in glb_id_vars data:", nrow(id_vars_dups_df))
myprint_df(id_vars_dups_df)
} else {
# glb_id_vars are unique across obs in both glb_<>_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
}
#pairs(subset(glb_trnent_df, select=-c(col_symbol)))
# Check for glb_newent_df & glb_trnent_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnent_df, <col1_name> == max(glb_trnent_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnent_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnent_df[which.max(glb_trnent_df$<col_name>),])
# print(<col_name>_freq_glb_trnent_df <- mycreate_tbl_df(glb_trnent_df, "<col_name>"))
# print(which.min(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>)[, 2]))
# print(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>))
# print(table(is.na(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(table(sign(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(mycreate_xtab(glb_trnent_df, <col1_name>))
# print(mycreate_xtab(glb_trnent_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mycreate_xtab(glb_trnent_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnent_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnent_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnent_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnent_df$<col1_name>.NA, glb_trnent_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnent_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_entity_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="manage_missing_data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed2 inspectORexplore.data 2 1 0.538
## elapsed3 manage_missing_data 2 2 1.620
2.2: manage missing data# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# glb_trnent_df <- na.omit(glb_trnent_df)
# glb_newent_df <- na.omit(glb_newent_df)
# df[is.na(df)] <- 0
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function(entity_df, newent_df) {
if (!glb_is_separate_newent_dataset) {
# Combine entity & newent
union_df <- rbind(mutate(entity_df, .src = "entity"),
mutate(newent_df, .src = "newent"))
union_imputed_df <- union_df[, setdiff(setdiff(names(entity_df),
glb_rsp_var),
glb_exclude_vars_as_features)]
print(summary(union_imputed_df))
require(mice)
set.seed(glb_mice_complete.seed)
union_imputed_df <- complete(mice(union_imputed_df))
print(summary(union_imputed_df))
union_df[, names(union_imputed_df)] <- union_imputed_df[, names(union_imputed_df)]
print(summary(union_df))
# union_df$.rownames <- rownames(union_df)
# union_df <- orderBy(~.rownames, union_df)
#
# imp_entity_df <- myimport_data(
# url="<imputed_trnng_url>",
# comment="imp_entity_df", force_header=TRUE, print_diagn=TRUE)
# print(all.equal(subset(union_df, select=-c(.src, .rownames, .rnorm)),
# imp_entity_df))
# Partition again
glb_trnent_df <<- subset(union_df, .src == "entity", select=-c(.src, .rownames))
comment(glb_trnent_df) <- "entity_df"
glb_newent_df <<- subset(union_df, .src == "newent", select=-c(.src, .rownames))
comment(glb_newent_df) <- "newent_df"
# Generate summaries
print(summary(entity_df))
print(sapply(names(entity_df), function(col) sum(is.na(entity_df[, col]))))
print(summary(newent_df))
print(sapply(names(newent_df), function(col) sum(is.na(newent_df[, col]))))
} else stop("Not implemented yet")
}
if (glb_impute_na_data) {
if ((sum(sapply(names(glb_trnent_df),
function(col) sum(is.na(glb_trnent_df[, col])))) > 0) |
(sum(sapply(names(glb_newent_df),
function(col) sum(is.na(glb_newent_df[, col])))) > 0))
glb_impute_missing_data(glb_trnent_df, glb_newent_df)
}
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="encodeORretype.data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed3 manage_missing_data 2 2 1.620
## elapsed4 encodeORretype.data 2 3 2.161
2.3: encode/retype data# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnent_df <- mymap_codes(glb_trnent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newent_df <- mymap_codes(glb_newent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnent_df$<col_name>.fctr <- factor(glb_trnent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
# glb_newent_df$<col_name>.fctr <- factor(glb_newent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_entity_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_entity_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_entity_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_trnent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_trnent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_trnent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_newent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_newent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_newent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) glb_display_class_dstrb(glb_rsp_var)
}
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
## Avg Negative.fctr .n
## 1 0.0 N 337
## 2 -0.2 N 127
## 3 0.2 N 115
## 4 -0.4 N 84
## 5 -0.8 N 76
## 6 -1.0 Y 72
## Avg Negative.fctr .n
## 1 0.0 N 337
## 5 -0.8 N 76
## 10 -1.2 Y 39
## 12 1.0 N 25
## 13 -1.4 Y 23
## 17 -1.6 Y 9
## Avg Negative.fctr .n
## 16 1.2 N 11
## 17 -1.6 Y 9
## 18 1.4 N 9
## 19 1.6 N 9
## 20 1.8 N 5
## 21 2.0 N 2
## Avg Negative.fctr .n
## 1 0.0 N 236
## 2 -0.2 N 89
## 3 0.2 N 80
## 4 -0.4 N 59
## 5 -0.8 N 53
## 6 -1.0 Y 50
## Avg Negative.fctr .n
## 2 -0.2 N 89
## 12 1.0 N 18
## 13 -1.4 Y 16
## 15 -2.0 Y 12
## 18 1.4 N 6
## 21 2.0 N 1
## Avg Negative.fctr .n
## 16 1.2 N 8
## 17 -1.6 Y 6
## 18 1.4 N 6
## 19 1.6 N 6
## 20 1.8 N 4
## 21 2.0 N 1
## Avg Negative.fctr .n
## 1 0.0 N 101
## 2 -0.2 N 38
## 3 0.2 N 35
## 4 -0.4 N 25
## 5 -0.8 N 23
## 6 -1.0 Y 22
## Avg Negative.fctr .n
## 2 -0.2 N 38
## 4 -0.4 N 25
## 9 0.6 N 13
## 12 -1.8 Y 7
## 14 1.0 N 7
## 18 1.4 N 3
## Avg Negative.fctr .n
## 16 -1.6 Y 3
## 17 1.2 N 3
## 18 1.4 N 3
## 19 1.6 N 3
## 20 1.8 N 1
## 21 2.0 N 1
## Negative.fctr.N Negative.fctr.Y
## New 0.8472727 0.1527273
## Training 0.8472727 0.1527273
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="extract_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed4 encodeORretype.data 2 3 2.161
## elapsed5 extract_features 3 0 6.366
3: extract features#```{r extract_features, cache=FALSE, eval=glb_is_textual}
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnent_df$<col_name>), -2, na.pad=TRUE)
# glb_trnent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newent_df$<col_name>), -2, na.pad=TRUE)
# glb_newent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newent_df[1, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df) - 1,
# "<col_name>"]
# glb_newent_df[2, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df),
# "<col_name>"]
# glb_trnent_df <- mutate(glb_trnent_df,
# <new_col_name>=
# )
# glb_newent_df <- mutate(glb_newent_df,
# <new_col_name>=
# )
glb_txt_var <- c("Tweet") # or NULL
glb_append_stop_words <- c("apple") # or NULL
if (glb_is_textual) {
require(tm)
glb_corpus <- Corpus(VectorSource(glb_entity_df[, glb_txt_var]))
glb_corpus <- tm_map(glb_corpus, tolower)
glb_corpus <- tm_map(glb_corpus, PlainTextDocument)
glb_corpus <- tm_map(glb_corpus, removePunctuation)
glb_corpus <- tm_map(glb_corpus, removeWords,
c(glb_append_stop_words, stopwords("english")))
glb_corpus <- tm_map(glb_corpus, stemDocument)
full_freqs_DTM <- DocumentTermMatrix(glb_corpus)
full_freqs_vctr <- colSums(as.matrix(full_freqs_DTM))
names(full_freqs_vctr) <- dimnames(full_freqs_DTM)[[2]]
full_freqs_df <- as.data.frame(full_freqs_vctr)
names(full_freqs_df) <- "freq.full"
full_freqs_df$term <- rownames(full_freqs_df)
full_freqs_df <- orderBy(~ -freq.full, full_freqs_df)
# ggplot(full_freqs_df, aes(x=freq.full)) + stat_ecdf()
# print(myplot_hbar(head(full_freqs_df, 10), "term", "freq.full"))
sprs_threshold <- 0.995
sprs_freqs_DTM <- removeSparseTerms(full_freqs_DTM, sprs_threshold)
sprs_freqs_vctr <- colSums(as.matrix(sprs_freqs_DTM))
names(sprs_freqs_vctr) <- dimnames(sprs_freqs_DTM)[[2]]
sprs_freqs_df <- as.data.frame(sprs_freqs_vctr)
names(sprs_freqs_df) <- "freq.sprs"
sprs_freqs_df$term <- rownames(sprs_freqs_df)
sprs_freqs_df <- orderBy(~ -freq.sprs, sprs_freqs_df)
# ggplot(sprs_freqs_df, aes(x=freq.sprs)) + stat_ecdf()
# print(myplot_hbar(head(sprs_freqs_df, 10), "term", "freq.sprs"))
terms_freqs_df <- merge(full_freqs_df, sprs_freqs_df, all.x=TRUE)
melt_freqs_df <- orderBy(~ -value, melt(terms_freqs_df, id.var="term"))
print(ggplot(melt_freqs_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=1-sprs_threshold, linetype = "dotted"))
print(myplot_hbar(head(melt_freqs_df, 20), "term", "value", colorcol_name="variable"))
melt_freqs_df <- orderBy(~ -value, melt(subset(terms_freqs_df, is.na(freq.sprs)), id.var="term"))
print(myplot_hbar(head(melt_freqs_df, 10), "term", "value", colorcol_name="variable"))
# Create txt features
txt_X_df <- as.data.frame(as.matrix(sprs_freqs_DTM))
colnames(txt_X_df) <- make.names(colnames(txt_X_df))
# Add dependent variable
glb_entity_df <- cbind(glb_entity_df, txt_X_df)
# a working copy of this is reqd in manage.missingdata chunk
union_df <- rbind(mutate(glb_trnent_df, .src = "trnent"),
mutate(glb_newent_df, .src = "newent"))
if (is.null(glb_id_vars)) {
union_df$.rownames <- rownames(union_df)
tmp_entity_df <- glb_entity_df
tmp_entity_df$.rownames <- rownames(tmp_entity_df)
mrg_entity_df <- merge(tmp_entity_df, union_df[, c(".src", ".rownames")])
} else stop("not implemented yet")
# Partition again
glb_trnent_df <- subset(mrg_entity_df, .src == "trnent", select=-c(.src, .rownames))
comment(glb_trnent_df) <- "trnent_df"
glb_newent_df <- subset(mrg_entity_df, .src == "newent", select=-c(.src, .rownames))
comment(glb_newent_df) <- "newent_df"
# Generate summaries
print(summary(glb_entity_df))
print(sapply(names(glb_entity_df), function(col) sum(is.na(glb_entity_df[, col]))))
print(summary(glb_trnent_df))
print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
print(summary(glb_newent_df))
print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
}
## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate
## Warning: Removed 6 rows containing missing values (geom_path).
## Warning in data.row.names(row.names, rowsi, i): some row.names duplicated:
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## Tweet Avg
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## {#IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune} : 9
## C #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 8
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune #IPOD GENERATION : 7
## FOLLOW @APPLE NOW : 5
## #IPODPLAYERPROMO #IPOD #IPAD #ITUNES #APPLE #PROMO#IPODPLAYERPROMO @apple @itune : 5
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## Median :0.00000
## Mean :0.01404
## 3rd Qu.:0.00000
## Max. :1.00000
##
## Tweet Avg Tweet.fctr
## 0 0 0
## .rnorm Negative.fctr X244tsuyoponzu
## 0 0 0
## X7evenstarz actual add
## 0 0 0
## alreadi alway amaz
## 0 0 0
## amazon android announc
## 0 0 0
## anyon app appl
## 0 0 0
## appstor arent ask
## 0 0 0
## avail away awesom
## 0 0 0
## back batteri best
## 0 0 0
## better big bit
## 0 0 0
## black blackberri break.
## 0 0 0
## bring burberri busi
## 0 0 0
## buy call can
## 0 0 0
## cant carbon card
## 0 0 0
## care case cdp
## 0 0 0
## chang charg charger
## 0 0 0
## cheap china color
## 0 0 0
## colour come compani
## 0 0 0
## condescens condom copi
## 0 0 0
## crack creat custom
## 0 0 0
## darn data date
## 0 0 0
## day dear design
## 0 0 0
## develop devic didnt
## 0 0 0
## die differ disappoint
## 0 0 0
## discontinu divulg doesnt
## 0 0 0
## done dont download
## 0 0 0
## drop email emiss
## 0 0 0
## emoji even event
## 0 0 0
## ever everi everyth
## 0 0 0
## facebook fail featur
## 0 0 0
## feel femal figur
## 0 0 0
## final finger fingerprint
## 0 0 0
## fire first fix
## 0 0 0
## follow freak free
## 0 0 0
## fun generat genius
## 0 0 0
## get give gold
## 0 0 0
## gonna good googl
## 0 0 0
## got great guess
## 0 0 0
## guy happen happi
## 0 0 0
## hate help hey
## 0 0 0
## hope hour httpbitly18xc8dk
## 0 0 0
## ibrooklynb idea ill
## 0 0 0
## imessag impress improv
## 0 0 0
## innov instead internet
## 0 0 0
## ios7 ipad iphon
## 0 0 0
## iphone4 iphone5 iphone5c
## 0 0 0
## iphoto ipod ipodplayerpromo
## 0 0 0
## isnt itun ive
## 0 0 0
## job just keynot
## 0 0 0
## know last launch
## 0 0 0
## let life like
## 0 0 0
## line lmao lock
## 0 0 0
## lol look los
## 0 0 0
## lost love mac
## 0 0 0
## macbook made make
## 0 0 0
## man mani market
## 0 0 0
## mayb mean microsoft
## 0 0 0
## mishiza miss mobil
## 0 0 0
## money motorola move
## 0 0 0
## much music natz0711
## 0 0 0
## need never new
## 0 0 0
## news next. nfc
## 0 0 0
## nokia noth now
## 0 0 0
## nsa nuevo offer
## 0 0 0
## old one page
## 0 0 0
## para peopl perfect
## 0 0 0
## person phone photog
## 0 0 0
## photographi pictur plastic
## 0 0 0
## play pleas ppl
## 0 0 0
## preorder price print
## 0 0 0
## pro problem product
## 0 0 0
## promo promoipodplayerpromo put
## 0 0 0
## que quiet read
## 0 0 0
## realli recommend refus
## 0 0 0
## releas right said
## 0 0 0
## samsung samsungsa say
## 0 0 0
## scanner screen secur
## 0 0 0
## see seem sell
## 0 0 0
## send servic shame
## 0 0 0
## share short show
## 0 0 0
## simpl sinc siri
## 0 0 0
## smart smartphon someth
## 0 0 0
## soon stand start
## 0 0 0
## steve still stop
## 0 0 0
## store stuff stupid
## 0 0 0
## suck support sure
## 0 0 0
## switch take talk
## 0 0 0
## team tech technolog
## 0 0 0
## tell text thank
## 0 0 0
## that theyr thing
## 0 0 0
## think tho thought
## 0 0 0
## time today togeth
## 0 0 0
## touch touchid tri
## 0 0 0
## true turn twitter
## 0 0 0
## two updat upgrad
## 0 0 0
## use user via
## 0 0 0
## video wait want
## 0 0 0
## watch way week
## 0 0 0
## well what white
## 0 0 0
## will windowsphon wish
## 0 0 0
## without wonder wont
## 0 0 0
## work world worst
## 0 0 0
## wow wtf yall
## 0 0 0
## year yes yet
## 0 0 0
## yooo your
## 0 0
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# print(myplot_scatter(glb_trnent_df, "<col1_name>", "<col2_name>", smooth=TRUE))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="select_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed5 extract_features 3 0 6.366
## elapsed6 select_features 4 0 14.222
4: select featuresprint(glb_feats_df <- myselect_features(entity_df=glb_trnent_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))
## id cor.y exclude.as.feat
## Avg Avg -0.7061583707 1
## Tweet.fctr Tweet.fctr 0.6354986940 1
## freak freak 0.4220126658 0
## hate hate 0.2150466216 0
## stuff stuff 0.1685214122 0
## suck suck 0.1644037549 0
## pictur pictur 0.1642996876 0
## wtf wtf 0.1642996876 0
## cant cant 0.1518891001 0
## shame shame 0.1405061949 0
## stupid stupid 0.1186461031 0
## even even 0.1158940633 0
## line line 0.1158940633 0
## yooo yooo 0.1158940633 0
## better better 0.1114365401 0
## ever ever 0.1076729878 0
## fix fix 0.1076729878 0
## charger charger 0.1069355141 0
## still still 0.1007505594 0
## charg charg 0.0970913594 0
## disappoint disappoint 0.0970913594 0
## short short 0.0970913594 0
## like like 0.0962377709 0
## ipod ipod -0.0852874403 0
## amazon amazon 0.0851663937 0
## yall yall 0.0851663937 0
## promoipodplayerpromo promoipodplayerpromo -0.0838913688 0
## break. break. 0.0826293697 0
## imessag imessag 0.0826293697 0
## stand stand 0.0826293697 0
## togeth togeth 0.0826293697 0
## ipodplayerpromo ipodplayerpromo -0.0793460925 0
## cheap cheap 0.0769587757 0
## wont wont 0.0758516866 0
## make make 0.0727362480 0
## carbon carbon 0.0709359262 0
## darn darn 0.0709359262 0
## httpbitly18xc8dk httpbitly18xc8dk 0.0709359262 0
## dear dear 0.0681568527 0
## facebook facebook 0.0681568527 0
## X7evenstarz X7evenstarz 0.0673843716 0
## condom condom 0.0673843716 0
## femal femal 0.0673843716 0
## money money 0.0673843716 0
## theyr theyr 0.0673843716 0
## ipad ipad -0.0646730918 0
## iphone5c iphone5c -0.0645077026 0
## batteri batteri 0.0626301156 0
## china china 0.0625002078 0
## turn turn 0.0625002078 0
## iphone5 iphone5 -0.0621379328 0
## promo promo -0.0615836556 0
## samsung samsung -0.0615836556 0
## hope hope 0.0611434911 0
## life life 0.0611434911 0
## sinc sinc 0.0611434911 0
## steve steve 0.0611434911 0
## switch switch 0.0611434911 0
## love love -0.0577763105 0
## everi everi 0.0564127109 0
## announc announc -0.0557827956 0
## last last 0.0549744869 0
## your your 0.0546349290 0
## lol lol -0.0537205371 0
## amaz amaz 0.0536765239 0
## arent arent 0.0536765239 0
## date date 0.0536765239 0
## divulg divulg 0.0536765239 0
## ill ill 0.0536765239 0
## ive ive 0.0536765239 0
## lost lost 0.0536765239 0
## noth noth 0.0536765239 0
## worst worst 0.0536765239 0
## new new -0.0536221452 0
## phone phone 0.0536044876 0
## appl appl -0.0533458207 0
## care care 0.0527276703 0
## way way 0.0527276703 0
## year year 0.0527276703 0
## updat updat 0.0519176496 0
## app app 0.0506802204 0
## well well -0.0506130885 0
## iphon iphon 0.0473646206 0
## help help -0.0470292313 0
## print print -0.0470292313 0
## servic servic -0.0470292313 0
## tech tech -0.0470292313 0
## tell tell -0.0470292313 0
## will will -0.0463979478 0
## news news -0.0453528423 0
## data data 0.0453496159 0
## take take 0.0453496159 0
## microsoft microsoft -0.0450714475 0
## itun itun -0.0449613971 0
## finger finger -0.0445884997 0
## instead instead -0.0445884997 0
## wait wait -0.0445884997 0
## card card 0.0429730405 0
## custom custom 0.0429730405 0
## die die 0.0429730405 0
## event event 0.0429730405 0
## problem problem 0.0429730405 0
## refus refus 0.0429730405 0
## two two 0.0429730405 0
## gold gold -0.0428877879 0
## mobil mobil -0.0428877879 0
## que que -0.0428877879 0
## avail avail -0.0420127055 0
## busi busi -0.0420127055 0
## follow follow -0.0420127055 0
## generat generat -0.0420127055 0
## wish wish -0.0420127055 0
## back back -0.0405914861 0
## googl googl -0.0405914861 0
## use use 0.0401415689 0
## big big -0.0392752586 0
## emoji emoji -0.0392752586 0
## iphoto iphoto -0.0392752586 0
## motorola motorola -0.0392752586 0
## touchid touchid -0.0392752586 0
## screen screen 0.0387788990 0
## thing thing 0.0387788990 0
## buy buy 0.0380283000 0
## burberri burberri -0.0375405647 0
## develop develop -0.0375405647 0
## video video -0.0375405647 0
## week week -0.0375405647 0
## fire fire -0.0363396181 0
## great great -0.0363396181 0
## keynot keynot -0.0363396181 0
## mayb mayb -0.0363396181 0
## mishiza mishiza -0.0363396181 0
## natz0711 natz0711 -0.0363396181 0
## offer offer -0.0363396181 0
## para para -0.0363396181 0
## samsungsa samsungsa -0.0363396181 0
## show show -0.0363396181 0
## watch watch -0.0363396181 0
## world world -0.0363396181 0
## need need -0.0357398233 0
## tri tri 0.0356343761 0
## actual actual -0.0346046348 0
## bring bring -0.0346046348 0
## share share -0.0346046348 0
## white white -0.0346046348 0
## cdp cdp 0.0341988646 0
## doesnt doesnt 0.0341988646 0
## emiss emiss 0.0341988646 0
## feel feel 0.0341988646 0
## fun fun 0.0341988646 0
## got got 0.0341988646 0
## hour hour 0.0341988646 0
## macbook macbook 0.0341988646 0
## miss miss 0.0341988646 0
## siri siri 0.0341988646 0
## start start 0.0341988646 0
## upgrad upgrad 0.0341988646 0
## get get 0.0332332539 0
## X244tsuyoponzu X244tsuyoponzu -0.0331531471 0
## appstor appstor -0.0331531471 0
## away away -0.0331531471 0
## creat creat -0.0331531471 0
## design design -0.0331531471 0
## happi happi -0.0331531471 0
## ibrooklynb ibrooklynb -0.0331531471 0
## launch launch -0.0331531471 0
## lock lock -0.0331531471 0
## page page -0.0331531471 0
## play play -0.0331531471 0
## someth someth -0.0331531471 0
## talk talk -0.0331531471 0
## touch touch -0.0331531471 0
## wonder wonder -0.0331531471 0
## devic devic -0.0325564924 0
## think think 0.0314859024 0
## anyon anyon -0.0305482407 0
## copi copi 0.0303310278 0
## guess guess 0.0303310278 0
## person person 0.0303310278 0
## smart smart 0.0303310278 0
## alway alway -0.0296350116 0
## condescens condescens -0.0296350116 0
## crack crack -0.0296350116 0
## discontinu discontinu -0.0296350116 0
## email email -0.0296350116 0
## figur figur -0.0296350116 0
## internet internet -0.0296350116 0
## iphone4 iphone4 -0.0296350116 0
## lmao lmao -0.0296350116 0
## los los -0.0296350116 0
## move move -0.0296350116 0
## nuevo nuevo -0.0296350116 0
## perfect perfect -0.0296350116 0
## photog photog -0.0296350116 0
## photographi photographi -0.0296350116 0
## quiet quiet -0.0296350116 0
## recommend recommend -0.0296350116 0
## send send -0.0296350116 0
## tho tho -0.0296350116 0
## true true -0.0296350116 0
## user user -0.0296350116 0
## best best 0.0267580922 0
## differ differ 0.0267580922 0
## nsa nsa 0.0267580922 0
## product product 0.0267580922 0
## ios7 ios7 -0.0266634900 0
## peopl peopl -0.0266634900 0
## releas releas -0.0266634900 0
## say say -0.0266634900 0
## store store -0.0259113719 0
## add add -0.0256490570 0
## alreadi alreadi -0.0256490570 0
## final final -0.0256490570 0
## genius genius -0.0256490570 0
## nfc nfc -0.0256490570 0
## pro pro -0.0256490570 0
## yes yes -0.0256490570 0
## just just -0.0237594870 0
## ppl ppl -0.0223147692 0
## price price -0.0216753537 0
## everyth everyth -0.0209296403 0
## music music -0.0209296403 0
## read read -0.0209296403 0
## without without -0.0209296403 0
## twitter twitter 0.0207137058 0
## old old 0.0202889470 0
## see see 0.0202889470 0
## color color -0.0199770086 0
## compani compani -0.0199770086 0
## know know -0.0199770086 0
## good good -0.0199770086 0
## let let -0.0199770086 0
## stop stop -0.0199770086 0
## download download 0.0188746800 0
## idea idea 0.0188746800 0
## job job 0.0188746800 0
## mani mani 0.0188746800 0
## simpl simpl 0.0188746800 0
## soon soon 0.0188746800 0
## team team 0.0188746800 0
## technolog technolog 0.0188746800 0
## wow wow 0.0188746800 0
## much much 0.0183336696 0
## support support 0.0183336696 0
## text text 0.0183336696 0
## come come -0.0172779254 0
## free free -0.0162362822 0
## innov innov -0.0162362822 0
## via via -0.0162362822 0
## time time 0.0159958708 0
## android android 0.0149137629 0
## black black -0.0148064982 0
## that that 0.0145566668 0
## want want -0.0141452654 0
## fingerprint fingerprint 0.0129942941 0
## now now 0.0129148701 0
## .rnorm .rnorm -0.0127552181 0
## scanner scanner -0.0121497764 0
## guy guy -0.0109788835 0
## pleas pleas -0.0108398425 0
## ask ask 0.0102616884 0
## colour colour 0.0102616884 0
## gonna gonna 0.0102616884 0
## happen happen 0.0102616884 0
## man man 0.0102616884 0
## give give 0.0094009452 0
## hey hey 0.0094009452 0
## today today 0.0094009452 0
## case case 0.0086651648 0
## chang chang 0.0086651648 0
## blackberri blackberri -0.0076273067 0
## mac mac -0.0076273067 0
## never never -0.0076273067 0
## plastic plastic -0.0076273067 0
## right right -0.0076273067 0
## sell sell -0.0076273067 0
## one one 0.0070552161 0
## market market -0.0068841106 0
## work work 0.0057872663 0
## thank thank -0.0049626450 0
## day day 0.0047078144 0
## nokia nokia 0.0047078144 0
## preorder preorder 0.0047078144 0
## next. next. -0.0036050106 0
## look look 0.0035739823 0
## awesom awesom 0.0033167112 0
## done done 0.0033167112 0
## featur featur 0.0033167112 0
## said said 0.0033167112 0
## secur secur 0.0033167112 0
## seem seem 0.0033167112 0
## smartphon smartphon 0.0033167112 0
## thought thought 0.0033167112 0
## what what 0.0033167112 0
## windowsphon windowsphon 0.0033167112 0
## bit bit 0.0028697294 0
## call call -0.0025381970 0
## didnt didnt -0.0025381970 0
## drop drop -0.0025381970 0
## fail fail -0.0025381970 0
## first first -0.0025381970 0
## improv improv -0.0025381970 0
## isnt isnt -0.0025381970 0
## made made -0.0025381970 0
## mean mean -0.0025381970 0
## put put -0.0025381970 0
## yet yet -0.0025381970 0
## impress impress -0.0022363540 0
## realli realli 0.0020955452 0
## can can 0.0017524556 0
## dont dont -0.0006481129 0
## sure sure 0.0003935570 0
## cor.y.abs
## Avg 0.7061583707
## Tweet.fctr 0.6354986940
## freak 0.4220126658
## hate 0.2150466216
## stuff 0.1685214122
## suck 0.1644037549
## pictur 0.1642996876
## wtf 0.1642996876
## cant 0.1518891001
## shame 0.1405061949
## stupid 0.1186461031
## even 0.1158940633
## line 0.1158940633
## yooo 0.1158940633
## better 0.1114365401
## ever 0.1076729878
## fix 0.1076729878
## charger 0.1069355141
## still 0.1007505594
## charg 0.0970913594
## disappoint 0.0970913594
## short 0.0970913594
## like 0.0962377709
## ipod 0.0852874403
## amazon 0.0851663937
## yall 0.0851663937
## promoipodplayerpromo 0.0838913688
## break. 0.0826293697
## imessag 0.0826293697
## stand 0.0826293697
## togeth 0.0826293697
## ipodplayerpromo 0.0793460925
## cheap 0.0769587757
## wont 0.0758516866
## make 0.0727362480
## carbon 0.0709359262
## darn 0.0709359262
## httpbitly18xc8dk 0.0709359262
## dear 0.0681568527
## facebook 0.0681568527
## X7evenstarz 0.0673843716
## condom 0.0673843716
## femal 0.0673843716
## money 0.0673843716
## theyr 0.0673843716
## ipad 0.0646730918
## iphone5c 0.0645077026
## batteri 0.0626301156
## china 0.0625002078
## turn 0.0625002078
## iphone5 0.0621379328
## promo 0.0615836556
## samsung 0.0615836556
## hope 0.0611434911
## life 0.0611434911
## sinc 0.0611434911
## steve 0.0611434911
## switch 0.0611434911
## love 0.0577763105
## everi 0.0564127109
## announc 0.0557827956
## last 0.0549744869
## your 0.0546349290
## lol 0.0537205371
## amaz 0.0536765239
## arent 0.0536765239
## date 0.0536765239
## divulg 0.0536765239
## ill 0.0536765239
## ive 0.0536765239
## lost 0.0536765239
## noth 0.0536765239
## worst 0.0536765239
## new 0.0536221452
## phone 0.0536044876
## appl 0.0533458207
## care 0.0527276703
## way 0.0527276703
## year 0.0527276703
## updat 0.0519176496
## app 0.0506802204
## well 0.0506130885
## iphon 0.0473646206
## help 0.0470292313
## print 0.0470292313
## servic 0.0470292313
## tech 0.0470292313
## tell 0.0470292313
## will 0.0463979478
## news 0.0453528423
## data 0.0453496159
## take 0.0453496159
## microsoft 0.0450714475
## itun 0.0449613971
## finger 0.0445884997
## instead 0.0445884997
## wait 0.0445884997
## card 0.0429730405
## custom 0.0429730405
## die 0.0429730405
## event 0.0429730405
## problem 0.0429730405
## refus 0.0429730405
## two 0.0429730405
## gold 0.0428877879
## mobil 0.0428877879
## que 0.0428877879
## avail 0.0420127055
## busi 0.0420127055
## follow 0.0420127055
## generat 0.0420127055
## wish 0.0420127055
## back 0.0405914861
## googl 0.0405914861
## use 0.0401415689
## big 0.0392752586
## emoji 0.0392752586
## iphoto 0.0392752586
## motorola 0.0392752586
## touchid 0.0392752586
## screen 0.0387788990
## thing 0.0387788990
## buy 0.0380283000
## burberri 0.0375405647
## develop 0.0375405647
## video 0.0375405647
## week 0.0375405647
## fire 0.0363396181
## great 0.0363396181
## keynot 0.0363396181
## mayb 0.0363396181
## mishiza 0.0363396181
## natz0711 0.0363396181
## offer 0.0363396181
## para 0.0363396181
## samsungsa 0.0363396181
## show 0.0363396181
## watch 0.0363396181
## world 0.0363396181
## need 0.0357398233
## tri 0.0356343761
## actual 0.0346046348
## bring 0.0346046348
## share 0.0346046348
## white 0.0346046348
## cdp 0.0341988646
## doesnt 0.0341988646
## emiss 0.0341988646
## feel 0.0341988646
## fun 0.0341988646
## got 0.0341988646
## hour 0.0341988646
## macbook 0.0341988646
## miss 0.0341988646
## siri 0.0341988646
## start 0.0341988646
## upgrad 0.0341988646
## get 0.0332332539
## X244tsuyoponzu 0.0331531471
## appstor 0.0331531471
## away 0.0331531471
## creat 0.0331531471
## design 0.0331531471
## happi 0.0331531471
## ibrooklynb 0.0331531471
## launch 0.0331531471
## lock 0.0331531471
## page 0.0331531471
## play 0.0331531471
## someth 0.0331531471
## talk 0.0331531471
## touch 0.0331531471
## wonder 0.0331531471
## devic 0.0325564924
## think 0.0314859024
## anyon 0.0305482407
## copi 0.0303310278
## guess 0.0303310278
## person 0.0303310278
## smart 0.0303310278
## alway 0.0296350116
## condescens 0.0296350116
## crack 0.0296350116
## discontinu 0.0296350116
## email 0.0296350116
## figur 0.0296350116
## internet 0.0296350116
## iphone4 0.0296350116
## lmao 0.0296350116
## los 0.0296350116
## move 0.0296350116
## nuevo 0.0296350116
## perfect 0.0296350116
## photog 0.0296350116
## photographi 0.0296350116
## quiet 0.0296350116
## recommend 0.0296350116
## send 0.0296350116
## tho 0.0296350116
## true 0.0296350116
## user 0.0296350116
## best 0.0267580922
## differ 0.0267580922
## nsa 0.0267580922
## product 0.0267580922
## ios7 0.0266634900
## peopl 0.0266634900
## releas 0.0266634900
## say 0.0266634900
## store 0.0259113719
## add 0.0256490570
## alreadi 0.0256490570
## final 0.0256490570
## genius 0.0256490570
## nfc 0.0256490570
## pro 0.0256490570
## yes 0.0256490570
## just 0.0237594870
## ppl 0.0223147692
## price 0.0216753537
## everyth 0.0209296403
## music 0.0209296403
## read 0.0209296403
## without 0.0209296403
## twitter 0.0207137058
## old 0.0202889470
## see 0.0202889470
## color 0.0199770086
## compani 0.0199770086
## know 0.0199770086
## good 0.0199770086
## let 0.0199770086
## stop 0.0199770086
## download 0.0188746800
## idea 0.0188746800
## job 0.0188746800
## mani 0.0188746800
## simpl 0.0188746800
## soon 0.0188746800
## team 0.0188746800
## technolog 0.0188746800
## wow 0.0188746800
## much 0.0183336696
## support 0.0183336696
## text 0.0183336696
## come 0.0172779254
## free 0.0162362822
## innov 0.0162362822
## via 0.0162362822
## time 0.0159958708
## android 0.0149137629
## black 0.0148064982
## that 0.0145566668
## want 0.0141452654
## fingerprint 0.0129942941
## now 0.0129148701
## .rnorm 0.0127552181
## scanner 0.0121497764
## guy 0.0109788835
## pleas 0.0108398425
## ask 0.0102616884
## colour 0.0102616884
## gonna 0.0102616884
## happen 0.0102616884
## man 0.0102616884
## give 0.0094009452
## hey 0.0094009452
## today 0.0094009452
## case 0.0086651648
## chang 0.0086651648
## blackberri 0.0076273067
## mac 0.0076273067
## never 0.0076273067
## plastic 0.0076273067
## right 0.0076273067
## sell 0.0076273067
## one 0.0070552161
## market 0.0068841106
## work 0.0057872663
## thank 0.0049626450
## day 0.0047078144
## nokia 0.0047078144
## preorder 0.0047078144
## next. 0.0036050106
## look 0.0035739823
## awesom 0.0033167112
## done 0.0033167112
## featur 0.0033167112
## said 0.0033167112
## secur 0.0033167112
## seem 0.0033167112
## smartphon 0.0033167112
## thought 0.0033167112
## what 0.0033167112
## windowsphon 0.0033167112
## bit 0.0028697294
## call 0.0025381970
## didnt 0.0025381970
## drop 0.0025381970
## fail 0.0025381970
## first 0.0025381970
## improv 0.0025381970
## isnt 0.0025381970
## made 0.0025381970
## mean 0.0025381970
## put 0.0025381970
## yet 0.0025381970
## impress 0.0022363540
## realli 0.0020955452
## can 0.0017524556
## dont 0.0006481129
## sure 0.0003935570
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="remove_correlated_features",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor
## elapsed6 select_features 4 0
## elapsed7 remove_correlated_features 4 1
## elapsed
## elapsed6 14.222
## elapsed7 15.261
5: fit modelsif (glb_is_classification && glb_is_binomial &&
(length(unique(glb_trnent_df[, glb_rsp_var])) < 2))
stop("glb_trnent_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_trnent_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_var <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[1, "id"]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_var != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_var, "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_var, " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
## [1] "fitting model: MFO.myMFO_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] N Y
## Levels: N Y
## [1] "unique.prob:"
## y
## N Y
## 0.8472727 0.1527273
## [1] "MFO.val:"
## [1] "N"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
##
## The following object is masked from 'package:stats':
##
## lowess
## [1] "in MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8472727 0.1527273
## 2 0.8472727 0.1527273
## 3 0.8472727 0.1527273
## 4 0.8472727 0.1527273
## 5 0.8472727 0.1527273
## 6 0.8472727 0.1527273
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.MFO.myMFO_classfr.N
## 1 N 699
## 2 Y 126
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.MFO.myMFO_classfr.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.MFO.myMFO_classfr.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 699 0
## Y 126 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.472727e-01 0.000000e+00 8.208848e-01 8.711498e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 5.237511e-01 8.390732e-29
## [1] "in MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## N Y
## 1 0.8472727 0.1527273
## 2 0.8472727 0.1527273
## 3 0.8472727 0.1527273
## 4 0.8472727 0.1527273
## 5 0.8472727 0.1527273
## 6 0.8472727 0.1527273
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.MFO.myMFO_classfr.N
## 1 N 300
## 2 Y 56
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.MFO.myMFO_classfr.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.MFO.myMFO_classfr.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 300 0
## Y 56 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.426966e-01 0.000000e+00 8.006423e-01 8.789342e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 5.355897e-01 1.986758e-13
## model_id model_method feats max.nTuningRuns
## 1 MFO.myMFO_classfr myMFO_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.304 0.002 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8472727
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8208848 0.8711498 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8426966
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8006423 0.8789342 0
if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
## [1] "fitting model: Random.myrandom_classfr"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "in Random.Classifier$prob"
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 593 110
## Y 106 16
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 593
## 2 Y 110
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 106
## 2 16
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.1290323
## 4 0.3 0.1290323
## 5 0.4 0.1290323
## 6 0.5 0.1290323
## 7 0.6 0.1290323
## 8 0.7 0.1290323
## 9 0.8 0.1290323
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 N 699
## 2 Y 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 699
## 2 126
## Prediction
## Reference N Y
## N 0 699
## Y 0 126
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.527273e-01 0.000000e+00 1.288502e-01 1.791152e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 1.342038e-153
## [1] "in Random.Classifier$prob"
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 260 48
## Y 40 8
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 260
## 2 Y 48
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 40
## 2 8
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.2718447
## 3 0.2 0.1538462
## 4 0.3 0.1538462
## 5 0.4 0.1538462
## 6 0.5 0.1538462
## 7 0.6 0.1538462
## 8 0.7 0.1538462
## 9 0.8 0.1538462
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.1000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 N 300
## 2 Y 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Random.myrandom_classfr.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Random.myrandom_classfr.Y
## 1 300
## 2 56
## Prediction
## Reference N Y
## N 0 300
## Y 0 56
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.573034e-01 0.000000e+00 1.210658e-01 1.993577e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 1.000000e+00 8.969796e-67
## model_id model_method feats max.nTuningRuns
## 1 Random.myrandom_classfr myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.226 0.001 0.4876695
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.1 0.2649842 0.1527273
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.1288502 0.1791152 0 0.5047619
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.1 0.2718447 0.1573034
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.1210658 0.1993577 0
# Any models that have tuning parameters has "better" results with cross-validation
# (except rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: freak"
## Loading required package: rpart
## Fitting cp = 0.214 on full training set
## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.2142857 0 1
##
## Node number 1: 825 observations
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273) *
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 699
## 2 Y 126
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 699 0
## Y 126 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.472727e-01 0.000000e+00 8.208848e-01 8.711498e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 5.237511e-01 8.390732e-29
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 300
## 2 Y 56
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.cv.0.rpart.Y
## 1 0
## 2 0
## Prediction
## Reference N Y
## N 300 0
## Y 56 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.426966e-01 0.000000e+00 8.006423e-01 8.789342e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 5.355897e-01 1.986758e-13
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart freak 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.59 0.015 0.5
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0 0.8472727
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8208848 0.8711498 0 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0 0.8426966
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8006423 0.8789342 0
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0,
tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: freak"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.2142857 0 1.0000000
## 2 0.0000000 1 0.7857143
##
## Variable importance
## freak
## 100
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.27511, (0 missing)
##
## Node number 2: 790 observations
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.3850932
## 4 0.3 0.3850932
## 5 0.4 0.3850932
## 6 0.5 0.3850932
## 7 0.6 0.3850932
## 8 0.7 0.3850932
## 9 0.8 0.3850932
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 4
## 2 31
## Prediction
## Reference N Y
## N 695 4
## Y 95 31
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.800000e-01 3.413572e-01 8.558521e-01 9.013914e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.212456e-03 1.491997e-19
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.2718447
## 3 0.2 0.4324324
## 4 0.3 0.4324324
## 5 0.4 0.4324324
## 6 0.5 0.4324324
## 7 0.6 0.4324324
## 8 0.7 0.4324324
## 9 0.8 0.4324324
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.cv.0.cp.0.rpart.Y
## 1 2
## 2 16
## Prediction
## Reference N Y
## N 298 2
## Y 40 16
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.820225e-01 3.853995e-01 8.438883e-01 9.136353e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 2.170565e-02 1.134925e-08
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart freak 0
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.449 0.013 0.6201546
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8 0.3850932 0.88
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8558521 0.9013914 0.3413572 0.6395238
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.8 0.4324324 0.8820225
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8438883 0.9136353 0.3853995
if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: freak"
## + Fold1: cp=0
## - Fold1: cp=0
## + Fold2: cp=0
## - Fold2: cp=0
## + Fold3: cp=0
## - Fold3: cp=0
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.107 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.2142857 0 1.0000000
## 2 0.0000000 1 0.7857143
##
## Variable importance
## freak
## 100
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.27511, (0 missing)
##
## Node number 2: 790 observations
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.3850932
## 4 0.3 0.3850932
## 5 0.4 0.3850932
## 6 0.5 0.3850932
## 7 0.6 0.3850932
## 8 0.7 0.3850932
## 9 0.8 0.3850932
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 4
## 2 31
## Prediction
## Reference N Y
## N 695 4
## Y 95 31
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.800000e-01 3.413572e-01 8.558521e-01 9.013914e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.212456e-03 1.491997e-19
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.2718447
## 3 0.2 0.4324324
## 4 0.3 0.4324324
## 5 0.4 0.4324324
## 6 0.5 0.4324324
## 7 0.6 0.4324324
## 8 0.7 0.4324324
## 9 0.8 0.4324324
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.rpart.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.rpart.Y
## 1 2
## 2 16
## Prediction
## Reference N Y
## N 298 2
## Y 40 16
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.820225e-01 3.853995e-01 8.438883e-01 9.136353e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 2.170565e-02 1.134925e-08
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart freak 3
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 0.956 0.013 0.6201546
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8 0.3850932 0.88
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8558521 0.9013914 0.3378153 0.6395238
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.8 0.4324324 0.8820225
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8438883 0.9136353 0.3853995
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01585054 0.1193248
# Used to compare vs. Interactions.High.cor.Y
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
## [1] "fitting model: Max.cor.Y.glm"
## [1] " indep_vars: freak"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0556 -0.5062 -0.5062 -0.5062 2.0583
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9901 0.1094 -18.194 < 2e-16 ***
## freak 3.9740 0.5435 7.312 2.62e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 705.23 on 824 degrees of freedom
## Residual deviance: 604.92 on 823 degrees of freedom
## AIC: 608.92
##
## Number of Fisher Scoring iterations: 5
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 699 124
## Y 0 2
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 699
## 2 Y 124
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 2
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.3850932
## 4 0.3 0.3850932
## 5 0.4 0.3850932
## 6 0.5 0.3850932
## 7 0.6 0.3850932
## 8 0.7 0.3850932
## 9 0.8 0.3850932
## 10 0.9 0.0312500
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 4
## 2 31
## Prediction
## Reference N Y
## N 695 4
## Y 95 31
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.800000e-01 3.413572e-01 8.558521e-01 9.013914e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.212456e-03 1.491997e-19
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 300 55
## Y 0 1
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 300
## 2 Y 55
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 1
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.27184466
## 2 0.1 0.27184466
## 3 0.2 0.43243243
## 4 0.3 0.43243243
## 5 0.4 0.43243243
## 6 0.5 0.43243243
## 7 0.6 0.43243243
## 8 0.7 0.43243243
## 9 0.8 0.43243243
## 10 0.9 0.03508772
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Max.cor.Y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Max.cor.Y.glm.Y
## 1 2
## 2 16
## Prediction
## Reference N Y
## N 298 2
## Y 40 16
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.820225e-01 3.853995e-01 8.438883e-01 9.136353e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 2.170565e-02 1.134925e-08
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.glm glm freak 1
## min.elapsedtime.everything min.elapsedtime.final max.auc.fit
## 1 1.091 0.015 0.6202001
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.8 0.3850932 0.88
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8558521 0.9013914 0.3378153 0.6395833
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.8 0.4324324 0.8820225
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8438883 0.9136353 0.3853995 608.9243
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01585054 0.1193248
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(unique(glb_feats_df$cor.high.X), NA)) > 0) {
# if (nrow(int_feats_df <- subset(glb_feats_df, !is.na(cor.high.X) &
# (exclude.as.feat == 0))) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
# This does not work - why ???
# indep_vars_vctr <- ifelse(glb_is_binomial,
# c(max_cor_y_x_var, paste(max_cor_y_x_var,
# subset(glb_feats_df, is.na(cor.low))[, "id"], sep=":")),
# union(max_cor_y_x_var, subset(glb_feats_df, is.na(cor.low))[, "id"]))
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_var, paste(max_cor_y_x_var, int_feats, sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_var, int_feats) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
}
## [1] "fitting model: Interact.High.cor.y.glm"
## [1] " indep_vars: freak, freak:cdp, freak:httpbitly18xc8dk, freak:femal, freak:refus, freak:emiss, freak:itun"
## + Fold1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1: parameter=none
## + Fold2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2: parameter=none
## + Fold3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0252 -0.5062 -0.5062 -0.5062 2.0583
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.9901 0.1094 -18.194 < 2e-16 ***
## freak 3.9031 0.5456 7.153 8.46e-13 ***
## `freak:cdp` NA NA NA NA
## `freak:httpbitly18xc8dk` NA NA NA NA
## `freak:femal` NA NA NA NA
## `freak:refus` NA NA NA NA
## `freak:emiss` NA NA NA NA
## `freak:itun` 12.6531 624.1941 0.020 0.984
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 705.23 on 824 degrees of freedom
## Residual deviance: 604.39 on 822 degrees of freedom
## AIC: 610.39
##
## Number of Fisher Scoring iterations: 13
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 699 122
## Y 0 4
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 699
## 2 Y 122
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 4
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.26498423
## 2 0.1 0.26498423
## 3 0.2 0.38509317
## 4 0.3 0.38509317
## 5 0.4 0.38509317
## 6 0.5 0.38509317
## 7 0.6 0.38509317
## 8 0.7 0.38509317
## 9 0.8 0.38509317
## 10 0.9 0.06153846
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Reference
## Prediction N Y
## N 695 95
## Y 4 31
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 695
## 2 Y 95
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 4
## 2 31
## Prediction
## Reference N Y
## N 695 4
## Y 95 31
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.800000e-01 3.413572e-01 8.558521e-01 9.013914e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.212456e-03 1.491997e-19
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 300 54
## Y 0 2
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 300
## 2 Y 54
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 2
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.27184466
## 2 0.1 0.27184466
## 3 0.2 0.43243243
## 4 0.3 0.43243243
## 5 0.4 0.43243243
## 6 0.5 0.43243243
## 7 0.6 0.43243243
## 8 0.7 0.43243243
## 9 0.8 0.43243243
## 10 0.9 0.06896552
## 11 1.0 0.00000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Reference
## Prediction N Y
## N 298 40
## Y 2 16
## Negative.fctr Negative.fctr.predict.Interact.High.cor.y.glm.N
## 1 N 298
## 2 Y 40
## Negative.fctr.predict.Interact.High.cor.y.glm.Y
## 1 2
## 2 16
## Prediction
## Reference N Y
## N 298 2
## Y 40 16
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.820225e-01 3.853995e-01 8.438883e-01 9.136353e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 2.170565e-02 1.134925e-08
## model_id model_method
## 1 Interact.High.cor.y.glm glm
## feats
## 1 freak, freak:cdp, freak:httpbitly18xc8dk, freak:femal, freak:refus, freak:emiss, freak:itun
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.928 0.033
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6202455 0.8 0.3850932 0.88
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8558521 0.9013914 0.3378153 0.6396429
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.8 0.4324324 0.8820225
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.8438883 0.9136353 0.3853995 610.3916
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01585054 0.1193248
# Low.cor.X
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
is.ConditionalX.y &
(exclude.as.feat != 1))[, "id"] else
indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
(exclude.as.feat != 1))[, "id"]
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
## [1] "fitting model: Low.cor.X.glm"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, yall, break., imessag, stand, togeth, cheap, wont, make, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5"
## + Fold1: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1: parameter=none
## + Fold2: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## - Fold2: parameter=none
## + Fold3: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3085 -0.1898 -0.0527 -0.0023 3.7379
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.4579 0.7889 -8.186 2.7e-16 ***
## freak 9.1809 1.3886 6.612 3.8e-11 ***
## hate 10.4590 3.0120 3.472 0.000516 ***
## stuff -2.0019 2.1442 -0.934 0.350497
## pictur 4.1991 2.8863 1.455 0.145716
## wtf 7.1805 2.8868 2.487 0.012868 *
## cant 4.0425 1.3180 3.067 0.002161 **
## shame 66.7249 2466.3610 0.027 0.978417
## stupid 11.5297 8.4770 1.360 0.173795
## even 6.3322 3.2007 1.978 0.047887 *
## line 1.9824 13.8581 0.143 0.886252
## yooo 3.2529 3.7166 0.875 0.381448
## better 2.6315 1.3363 1.969 0.048923 *
## ever 3.3362 4.6128 0.723 0.469523
## fix 4.9017 2.1968 2.231 0.025665 *
## charger 5.0938 1.9345 2.633 0.008460 **
## still 1.9025 1.8135 1.049 0.294147
## charg 0.6878 7.2813 0.094 0.924739
## disappoint 13.8308 4.6660 2.964 0.003035 **
## short 3.4861 4.1900 0.832 0.405410
## like 3.7224 1.2323 3.021 0.002523 **
## yall 1.1669 4.1452 0.281 0.778328
## break. 6.4745 2.3095 2.803 0.005057 **
## imessag 6.6843 3.6420 1.835 0.066452 .
## stand 4.3340 5.4653 0.793 0.427771
## togeth 7.0968 4.1727 1.701 0.088984 .
## cheap 2.3657 3.3719 0.702 0.482934
## wont -3.2771 2.2019 -1.488 0.136681
## make -1.7010 1.2881 -1.321 0.186634
## darn 5.5660 1.6493 3.375 0.000739 ***
## httpbitly18xc8dk 60.7538 2466.3732 0.025 0.980348
## dear 7.5988 3.1216 2.434 0.014921 *
## facebook -55.5605 2466.3738 -0.023 0.982027
## X7evenstarz 3.8727 10.2669 0.377 0.706020
## femal 4.9297 2.8880 1.707 0.087831 .
## money 3.3123 3.4895 0.949 0.342501
## theyr 9.8591 3.5911 2.745 0.006044 **
## batteri 3.0769 2.7941 1.101 0.270813
## china 5.3347 1.6584 3.217 0.001296 **
## turn -3.0405 3.8535 -0.789 0.430106
## hope 6.1148 3.4096 1.793 0.072909 .
## life 6.3707 1.8034 3.533 0.000411 ***
## sinc 3.2532 3.6091 0.901 0.367391
## steve -1.3674 3.5482 -0.385 0.699953
## switch 1.1880 3.7146 0.320 0.749108
## everi 4.7127 2.1334 2.209 0.027176 *
## last -21.9212 48676.8709 0.000 0.999641
## your 3.3579 2.1863 1.536 0.124569
## amaz -0.2129 5.7301 -0.037 0.970365
## arent 2.2988 3.9178 0.587 0.557368
## date -2.0176 6.2848 -0.321 0.748184
## ill 13.2760 56.8806 0.233 0.815450
## ive -2.0912 5.0578 -0.413 0.679271
## lost 2.7784 4.1408 0.671 0.502240
## noth 3.4509 5.5405 0.623 0.533388
## worst -0.8178 2.6712 -0.306 0.759477
## phone -0.2324 1.3605 -0.171 0.864341
## care 4.8121 2.6156 1.840 0.065803 .
## way -1.7727 2.3345 -0.759 0.447659
## year 4.0920 2.6147 1.565 0.117591
## updat -0.6827 2.2880 -0.298 0.765392
## app 2.2916 1.4806 1.548 0.121680
## iphon 1.2711 0.7686 1.654 0.098178 .
## data 5.1364 1.8282 2.810 0.004961 **
## take 2.9568 2.0412 1.449 0.147457
## card 1.2747 2.1017 0.607 0.544180
## custom -1.7334 5.3621 -0.323 0.746490
## die -0.9854 4.1027 -0.240 0.810188
## event 2.0671 3.1784 0.650 0.515457
## problem 6.4260 4.4629 1.440 0.149907
## two 7.2052 4.5104 1.597 0.110159
## use 1.4373 1.5047 0.955 0.339459
## screen 2.8497 2.7874 1.022 0.306605
## thing 3.0792 1.6215 1.899 0.057568 .
## buy 5.0214 1.9637 2.557 0.010553 *
## tri -4.0178 2.5437 -1.579 0.114224
## cdp 57.1267 2466.4167 0.023 0.981521
## doesnt -2.1867 3.9405 -0.555 0.578950
## emiss -65.3626 2466.4048 -0.027 0.978858
## feel -2.4023 5.7229 -0.420 0.674653
## fun 1.4037 2.7686 0.507 0.612137
## got 1.5122 3.5559 0.425 0.670652
## hour -0.1233 2.8784 -0.043 0.965844
## macbook 0.7301 3.3588 0.217 0.827909
## miss 7.0787 3.2160 2.201 0.027729 *
## siri -1.3719 4.6027 -0.298 0.765664
## start 3.2558 3.4045 0.956 0.338900
## upgrad 8.9117 2.7618 3.227 0.001252 **
## get -0.5138 1.2664 -0.406 0.684921
## think 2.5240 1.5250 1.655 0.097905 .
## copi 6.7733 4.1843 1.619 0.105504
## guess 4.5797 3.7528 1.220 0.222334
## person 8.0474 5.0483 1.594 0.110916
## smart -2.2304 4.4721 -0.499 0.617961
## best 4.5583 3.1502 1.447 0.147896
## differ 0.7338 2.3510 0.312 0.754948
## nsa 1.8094 2.0638 0.877 0.380642
## product 3.3203 2.6912 1.234 0.217293
## twitter -2.2748 2.1446 -1.061 0.288814
## old 3.5757 2.0715 1.726 0.084314 .
## see -9.3730 43.1672 -0.217 0.828106
## download -3.0606 5.2950 -0.578 0.563254
## idea 6.3016 2.5233 2.497 0.012512 *
## job -1.1333 4.6334 -0.245 0.806776
## mani 2.5557 3.4581 0.739 0.459887
## simpl -7.2732 6.2341 -1.167 0.243342
## soon 5.2217 5.2022 1.004 0.315500
## team 2.2988 4.5957 0.500 0.616924
## technolog 4.6206 8.9688 0.515 0.606418
## wow -5.4517 5.6398 -0.967 0.333721
## much 1.1447 3.3954 0.337 0.736011
## support -0.3005 2.3696 -0.127 0.899097
## text -2.4722 3.5759 -0.691 0.489337
## time 0.2476 2.1382 0.116 0.907797
## android 1.8172 1.5819 1.149 0.250654
## that 2.3894 2.6910 0.888 0.374571
## fingerprint 0.6778 2.0249 0.335 0.737810
## now 0.7887 1.4362 0.549 0.582891
## ask 0.7586 2.4126 0.314 0.753184
## colour 1.3380 2.7149 0.493 0.622125
## gonna -7.5310 6.8267 -1.103 0.269954
## happen -3.1729 41.1630 -0.077 0.938558
## man 3.2858 2.4052 1.366 0.171896
## give 1.3190 2.2237 0.593 0.553090
## hey -6.8528 3.7503 -1.827 0.067662 .
## today -3.2471 3.7967 -0.855 0.392419
## case 3.8488 3.4790 1.106 0.268596
## chang 4.9397 2.3426 2.109 0.034974 *
## one -1.4582 1.9308 -0.755 0.450092
## work -3.1891 3.7275 -0.856 0.392237
## day 5.2256 3.0478 1.715 0.086429 .
## nokia 0.8437 3.2609 0.259 0.795837
## preorder -6.6698 12.9653 -0.514 0.606950
## look -1.1209 1.8218 -0.615 0.538368
## awesom 2.6210 1.7157 1.528 0.126600
## done 5.7082 2.4483 2.331 0.019727 *
## featur -0.4649 6.0655 -0.077 0.938906
## said 4.8859 2.4953 1.958 0.050231 .
## secur 0.7089 3.5540 0.199 0.841898
## seem 0.5479 2.5813 0.212 0.831898
## smartphon 7.2719 4.3854 1.658 0.097275 .
## thought 7.9712 3.1405 2.538 0.011143 *
## what 1.9916 5.7571 0.346 0.729394
## windowsphon 4.1409 1.9940 2.077 0.037832 *
## bit 0.2055 1.6557 0.124 0.901244
## realli -5.5976 2.6374 -2.122 0.033803 *
## can -1.9029 1.6440 -1.158 0.247062
## sure -1.0157 2.4499 -0.415 0.678434
## dont -3.3268 2.3315 -1.427 0.153611
## impress -0.8317 4.1098 -0.202 0.839635
## call 5.0740 1.8913 2.683 0.007300 **
## didnt 0.3437 2.2320 0.154 0.877616
## drop -1.4784 4.3729 -0.338 0.735300
## fail -1.0601 2.7400 -0.387 0.698829
## first 6.1029 2.8523 2.140 0.032382 *
## improv 0.8695 3.3464 0.260 0.794996
## isnt -2.7901 4.1092 -0.679 0.497144
## made -2.6856 3.9414 -0.681 0.495629
## mean 0.1190 4.4485 0.027 0.978655
## put -1.4000 5.7340 -0.244 0.807103
## yet -6.0535 5.8116 -1.042 0.297587
## next. -0.3392 4.5548 -0.074 0.940629
## thank -0.4397 1.3702 -0.321 0.748267
## market 0.6426 3.0217 0.213 0.831589
## blackberri 2.6499 2.2374 1.184 0.236261
## mac 2.3323 3.3632 0.693 0.488011
## never -9.2031 57.1015 -0.161 0.871959
## plastic -5.0998 3.9296 -1.298 0.194358
## right 0.3378 3.1728 0.106 0.915220
## sell 4.3595 3.5363 1.233 0.217659
## pleas -3.0839 2.8663 -1.076 0.281967
## guy -3.6423 8.3832 -0.434 0.663946
## scanner -6.3453 4.5032 -1.409 0.158818
## .rnorm -0.3661 0.3324 -1.101 0.270690
## want 3.3752 1.6698 2.021 0.043250 *
## black -6.1676 3.1152 -1.980 0.047720 *
## free -6.1265 3.6945 -1.658 0.097260 .
## innov 1.8728 2.1722 0.862 0.388610
## via -2.4786 7.6336 -0.325 0.745408
## come -2.2611 2.3578 -0.959 0.337578
## good 0.2994 4.3154 0.069 0.944683
## let -2.0011 3.7586 -0.532 0.594452
## stop -2.1427 2.8574 -0.750 0.453323
## color -5.4951 3.5224 -1.560 0.118743
## compani -6.7036 4.6795 -1.433 0.151986
## know -3.9729 3.8882 -1.022 0.306878
## price -2.0591 3.8277 -0.538 0.590615
## just 0.2686 1.6299 0.165 0.869126
## store -1.6054 2.2636 -0.709 0.478167
## ios7 2.1131 3.0054 0.703 0.481985
## peopl 0.1478 2.9921 0.049 0.960605
## releas -6.9292 4.1049 -1.688 0.091403 .
## say -5.1095 3.3682 -1.517 0.129266
## anyon 3.1128 2.1092 1.476 0.139991
## devic -41.9680 1447.4615 -0.029 0.976869
## need -3.8647 2.1098 -1.832 0.066980 .
## back -4.5801 3.1253 -1.466 0.142781
## googl -5.3238 3.3140 -1.606 0.108168
## itun 0.9411 0.5375 1.751 0.079985 .
## microsoft -3.6143 2.3832 -1.517 0.129370
## will -3.9255 2.1233 -1.849 0.064486 .
## appl -1.0739 3.4113 -0.315 0.752901
## new -3.1416 1.6851 -1.864 0.062283 .
## iphone5 0.8216 1.6849 0.488 0.625829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 705.23 on 824 degrees of freedom
## Residual deviance: 215.13 on 621 degrees of freedom
## AIC: 623.13
##
## Number of Fisher Scoring iterations: 18
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 609 8
## Y 90 118
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 609
## 2 Y 8
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 90
## 2 118
## Reference
## Prediction N Y
## N 666 9
## Y 33 117
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 666
## 2 Y 9
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 33
## 2 117
## Reference
## Prediction N Y
## N 683 11
## Y 16 115
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 683
## 2 Y 11
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 16
## 2 115
## Reference
## Prediction N Y
## N 693 14
## Y 6 112
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 693
## 2 Y 14
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 6
## 2 112
## Reference
## Prediction N Y
## N 694 19
## Y 5 107
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 694
## 2 Y 19
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 5
## 2 107
## Reference
## Prediction N Y
## N 694 25
## Y 5 101
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 694
## 2 Y 25
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 5
## 2 101
## Reference
## Prediction N Y
## N 697 32
## Y 2 94
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 697
## 2 Y 32
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 2
## 2 94
## Reference
## Prediction N Y
## N 698 44
## Y 1 82
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 698
## 2 Y 44
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 1
## 2 82
## Reference
## Prediction N Y
## N 698 61
## Y 1 65
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 698
## 2 Y 61
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 1
## 2 65
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.7065868
## 3 0.2 0.8478261
## 4 0.3 0.8949416
## 5 0.4 0.9180328
## 6 0.5 0.8991597
## 7 0.6 0.8706897
## 8 0.7 0.8468468
## 9 0.8 0.7846890
## 10 0.9 0.6770833
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 693
## 2 Y 14
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 6
## 2 112
## Reference
## Prediction N Y
## N 693 14
## Y 6 112
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 693
## 2 Y 14
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 6
## 2 112
## Prediction
## Reference N Y
## N 693 6
## Y 14 112
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.757576e-01 9.038260e-01 9.628069e-01 9.851307e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.394732e-35 1.175249e-01
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 245 23
## Y 55 33
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 245
## 2 Y 23
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 55
## 2 33
## Reference
## Prediction N Y
## N 253 25
## Y 47 31
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 253
## 2 Y 25
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 47
## 2 31
## Reference
## Prediction N Y
## N 262 25
## Y 38 31
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 262
## 2 Y 25
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 38
## 2 31
## Reference
## Prediction N Y
## N 267 27
## Y 33 29
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 267
## 2 Y 27
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 33
## 2 29
## Reference
## Prediction N Y
## N 268 28
## Y 32 28
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 268
## 2 Y 28
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 32
## 2 28
## Reference
## Prediction N Y
## N 273 29
## Y 27 27
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 273
## 2 Y 29
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 27
## 2 27
## Reference
## Prediction N Y
## N 275 31
## Y 25 25
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 275
## 2 Y 31
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 25
## 2 25
## Reference
## Prediction N Y
## N 279 34
## Y 21 22
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 279
## 2 Y 34
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 21
## 2 22
## Reference
## Prediction N Y
## N 281 35
## Y 19 21
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 281
## 2 Y 35
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 19
## 2 21
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.4583333
## 3 0.2 0.4626866
## 4 0.3 0.4960000
## 5 0.4 0.4915254
## 6 0.5 0.4827586
## 7 0.6 0.4909091
## 8 0.7 0.4716981
## 9 0.8 0.4444444
## 10 0.9 0.4375000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.3000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 262
## 2 Y 25
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 38
## 2 31
## Reference
## Prediction N Y
## N 262 25
## Y 38 31
## Negative.fctr Negative.fctr.predict.Low.cor.X.glm.N
## 1 N 262
## 2 Y 25
## Negative.fctr.predict.Low.cor.X.glm.Y
## 1 38
## 2 31
## Prediction
## Reference N Y
## N 262 38
## Y 25 31
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8230337 0.3900794 0.7793438 0.8612518 0.8426966
## AccuracyPValue McnemarPValue
## 0.8619683 0.1305700
## model_id model_method
## 1 Low.cor.X.glm glm
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, yall, break., imessag, stand, togeth, cheap, wont, make, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 4.411 1.087
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9634853 0.4 0.9180328 0.750303
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9628069 0.9851307 0.2316521 0.7086607
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.3 0.496 0.8230337
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.7793438 0.8612518 0.3900794 623.1343
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.04963781 0.09400829
# All X that is not user excluded
if (glb_is_classification && glb_is_binomial) {
model_id_pfx <- "Conditional.X"
# indep_vars_vctr <- setdiff(names(glb_trnent_df), union(glb_rsp_var, glb_exclude_vars_as_features))
indep_vars_vctr <- subset(glb_feats_df, is.ConditionalX.y &
(exclude.as.feat != 1))[, "id"]
} else {
model_id_pfx <- "All.X"
indep_vars_vctr <- subset(glb_feats_df,
(exclude.as.feat != 1))[, "id"]
}
for (method in glb_models_method_vctr) {
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# Since caret does not optimize rpart well
if (method == "rpart")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# Compare how rf performs w/i & w/o .rnorm
if (method == "rf")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".no.rnorm"), model_method=method,
indep_vars_vctr=setdiff(indep_vars_vctr, c(".rnorm")),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
}
## [1] "fitting model: Conditional.X.glm"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## + Fold1: parameter=none
## Warning: glm.fit: algorithm did not converge
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1: parameter=none
## + Fold2: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2: parameter=none
## + Fold3: parameter=none
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred
## Warning: not plotting observations with leverage one:
## 98, 607, 788
## Warning: not plotting observations with leverage one:
## 98, 607, 788
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3211 -0.1780 -0.0512 -0.0013 3.7642
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.393e+00 7.872e-01 -8.121 4.61e-16 ***
## freak 9.162e+00 1.418e+00 6.463 1.03e-10 ***
## hate 1.032e+01 2.974e+00 3.469 0.000523 ***
## stuff -2.249e+00 2.136e+00 -1.053 0.292300
## pictur 5.058e+00 3.011e+00 1.680 0.092980 .
## wtf 7.351e+00 2.992e+00 2.457 0.014028 *
## cant 3.991e+00 1.316e+00 3.031 0.002434 **
## shame 7.109e+01 3.422e+03 0.021 0.983424
## stupid 1.139e+01 9.189e+00 1.240 0.215091
## even 5.968e+00 3.150e+00 1.895 0.058143 .
## line 3.364e+00 1.523e+01 0.221 0.825263
## yooo 3.220e+00 3.693e+00 0.872 0.383133
## better 2.561e+00 1.352e+00 1.895 0.058139 .
## ever 4.574e+00 4.796e+00 0.954 0.340256
## fix 4.833e+00 2.254e+00 2.144 0.032032 *
## charger 4.845e+00 2.033e+00 2.384 0.017147 *
## still 1.516e+00 1.980e+00 0.766 0.443832
## charg 3.536e-01 9.838e+00 0.036 0.971326
## disappoint 1.527e+01 5.021e+00 3.041 0.002361 **
## short 3.443e+00 5.509e+00 0.625 0.531927
## like 3.781e+00 1.252e+00 3.021 0.002520 **
## amazon -1.117e+01 2.360e+06 0.000 0.999996
## yall 1.388e+00 4.047e+00 0.343 0.731581
## break. 5.974e+00 2.455e+00 2.434 0.014940 *
## imessag 7.047e+00 3.689e+00 1.910 0.056113 .
## stand 4.191e+00 5.559e+00 0.754 0.450860
## togeth 7.190e+00 4.114e+00 1.747 0.080553 .
## cheap 2.555e+00 3.431e+00 0.745 0.456459
## wont -3.728e+00 2.253e+00 -1.654 0.098058 .
## make -1.434e+00 1.336e+00 -1.073 0.283207
## carbon -3.216e+00 2.360e+06 0.000 0.999999
## darn 5.507e+00 1.663e+00 3.312 0.000927 ***
## httpbitly18xc8dk -9.438e+00 2.404e+06 0.000 0.999997
## dear 7.578e+00 3.183e+00 2.380 0.017294 *
## facebook -1.307e+01 6.419e+03 -0.002 0.998376
## X7evenstarz 4.118e+00 1.010e+01 0.408 0.683565
## condom 4.951e+00 2.919e+00 1.696 0.089854 .
## femal NA NA NA NA
## money 2.766e+00 4.263e+00 0.649 0.516479
## theyr 1.017e+01 3.909e+00 2.601 0.009291 **
## batteri 3.158e+00 2.774e+00 1.138 0.255016
## china 5.322e+00 1.641e+00 3.243 0.001184 **
## turn -3.304e+00 3.930e+00 -0.841 0.400492
## hope 6.030e+00 3.360e+00 1.795 0.072712 .
## life 6.307e+00 1.834e+00 3.440 0.000582 ***
## sinc 2.916e+00 3.735e+00 0.781 0.434918
## steve -8.925e-01 3.589e+00 -0.249 0.803629
## switch 1.119e+00 3.680e+00 0.304 0.761102
## everi 4.744e+00 2.178e+00 2.178 0.029381 *
## last -2.291e+01 8.857e+04 0.000 0.999794
## your 3.139e+00 2.289e+00 1.371 0.170313
## amaz -6.639e-01 6.158e+00 -0.108 0.914138
## arent 2.263e+00 3.959e+00 0.572 0.567653
## date -2.813e+00 6.932e+00 -0.406 0.684833
## divulg 9.498e+01 4.115e+06 0.000 0.999982
## ill 1.458e+01 5.652e+01 0.258 0.796460
## ive -2.613e+00 5.211e+00 -0.501 0.616124
## lost 3.256e+00 3.906e+00 0.834 0.404518
## noth 2.259e+00 6.336e+00 0.357 0.721396
## worst -8.211e-01 2.685e+00 -0.306 0.759750
## phone -2.554e-01 1.390e+00 -0.184 0.854178
## care 4.786e+00 2.661e+00 1.798 0.072136 .
## way -1.669e+00 2.342e+00 -0.713 0.476110
## year 4.265e+00 2.672e+00 1.596 0.110434
## updat -8.471e-01 2.369e+00 -0.358 0.720653
## app 2.305e+00 1.510e+00 1.526 0.126935
## iphon 1.147e+00 7.847e-01 1.462 0.143647
## data 5.089e+00 1.831e+00 2.779 0.005454 **
## take 2.837e+00 2.132e+00 1.331 0.183319
## card 1.504e+00 2.181e+00 0.690 0.490399
## custom -2.434e+00 5.539e+00 -0.439 0.660344
## die -1.250e+00 4.055e+00 -0.308 0.757891
## event 2.133e+00 3.190e+00 0.669 0.503663
## problem 6.756e+00 4.466e+00 1.513 0.130383
## refus -4.381e+01 4.719e+06 0.000 0.999993
## two 1.021e+01 5.274e+00 1.936 0.052898 .
## use 1.069e+00 1.562e+00 0.684 0.493726
## screen 3.898e+00 2.830e+00 1.378 0.168343
## thing 3.145e+00 1.607e+00 1.958 0.050243 .
## buy 5.239e+00 2.030e+00 2.580 0.009868 **
## tri -3.757e+00 2.506e+00 -1.499 0.133810
## cdp 2.641e+01 2.360e+06 0.000 0.999991
## doesnt -2.414e+00 4.285e+00 -0.563 0.573121
## emiss -4.251e+01 4.719e+06 0.000 0.999993
## feel -2.301e+00 5.569e+00 -0.413 0.679443
## fun 1.108e+00 2.792e+00 0.397 0.691466
## got 1.312e+00 3.452e+00 0.380 0.703971
## hour 6.726e-02 2.842e+00 0.024 0.981120
## macbook 8.066e-01 3.288e+00 0.245 0.806190
## miss 9.230e+00 3.759e+00 2.456 0.014067 *
## siri -2.033e+00 4.638e+00 -0.438 0.661158
## start 2.984e+00 3.520e+00 0.848 0.396626
## upgrad 8.819e+00 2.690e+00 3.278 0.001044 **
## get -4.875e-01 1.281e+00 -0.380 0.703581
## think 2.573e+00 1.510e+00 1.703 0.088504 .
## copi 5.972e+00 4.265e+00 1.400 0.161445
## guess 7.445e+00 3.768e+00 1.976 0.048165 *
## person 7.979e+00 4.948e+00 1.613 0.106825
## smart -2.496e+00 4.861e+00 -0.514 0.607596
## best 4.180e+00 3.126e+00 1.337 0.181093
## differ 9.135e-01 2.401e+00 0.380 0.703643
## nsa 1.665e+00 2.111e+00 0.789 0.430264
## product 3.447e+00 2.563e+00 1.345 0.178751
## twitter -2.466e+00 2.274e+00 -1.084 0.278161
## old 3.916e+00 2.135e+00 1.834 0.066670 .
## see -9.845e+00 4.619e+01 -0.213 0.831221
## download -2.486e+00 5.139e+00 -0.484 0.628648
## idea 6.054e+00 2.689e+00 2.251 0.024385 *
## job -9.443e-01 4.722e+00 -0.200 0.841491
## mani 2.593e+00 3.580e+00 0.724 0.468827
## simpl -9.484e+00 6.561e+00 -1.445 0.148328
## soon 5.019e+00 5.177e+00 0.969 0.332362
## team 2.528e+00 4.421e+00 0.572 0.567371
## technolog 8.686e+00 1.885e+01 0.461 0.644873
## wow -5.301e+00 6.056e+00 -0.875 0.381414
## much 1.343e+00 3.469e+00 0.387 0.698698
## support -1.211e-01 2.687e+00 -0.045 0.964046
## text -2.910e+00 3.643e+00 -0.799 0.424517
## time -4.163e-01 2.291e+00 -0.182 0.855802
## android 1.711e+00 1.585e+00 1.079 0.280462
## that 2.091e+00 2.749e+00 0.761 0.446806
## fingerprint 6.299e-01 2.123e+00 0.297 0.766680
## now 7.270e-01 1.494e+00 0.486 0.626618
## ask 6.203e-01 2.486e+00 0.250 0.802953
## colour 1.009e+00 2.780e+00 0.363 0.716760
## gonna -8.251e+00 9.123e+00 -0.905 0.365730
## happen -4.242e+00 3.455e+01 -0.123 0.902277
## man 3.311e+00 2.401e+00 1.379 0.167794
## give 1.664e+00 2.363e+00 0.704 0.481339
## hey -7.179e+00 4.093e+00 -1.754 0.079423 .
## today -2.338e+00 3.834e+00 -0.610 0.542031
## case 4.744e+00 3.414e+00 1.389 0.164699
## chang 4.850e+00 2.330e+00 2.081 0.037393 *
## one -1.989e+00 2.106e+00 -0.944 0.345006
## work -3.400e+00 3.736e+00 -0.910 0.362687
## day 4.990e+00 3.129e+00 1.595 0.110812
## nokia 2.355e-02 3.533e+00 0.007 0.994683
## preorder -8.001e+00 1.452e+01 -0.551 0.581487
## look -1.369e+00 1.922e+00 -0.712 0.476345
## awesom 2.695e+00 1.747e+00 1.543 0.122915
## done 5.602e+00 2.475e+00 2.263 0.023611 *
## featur -6.516e-01 6.298e+00 -0.103 0.917594
## said 4.973e+00 2.508e+00 1.983 0.047393 *
## secur 5.288e-01 3.531e+00 0.150 0.880956
## seem 6.595e-01 2.655e+00 0.248 0.803805
## smartphon 8.578e+00 4.571e+00 1.877 0.060557 .
## thought 8.352e+00 3.608e+00 2.315 0.020638 *
## what 2.027e+00 6.135e+00 0.330 0.741131
## windowsphon 4.093e+00 1.972e+00 2.075 0.037969 *
## bit 1.877e-01 1.665e+00 0.113 0.910220
## realli -5.617e+00 2.663e+00 -2.109 0.034918 *
## can -1.702e+00 1.685e+00 -1.010 0.312460
## sure -8.192e-01 2.501e+00 -0.328 0.743249
## dont -3.202e+00 2.317e+00 -1.382 0.167028
## impress -3.330e-01 3.945e+00 -0.084 0.932729
## call 4.945e+00 1.909e+00 2.591 0.009581 **
## didnt 3.805e-01 2.251e+00 0.169 0.865797
## drop -3.880e+00 4.885e+00 -0.794 0.426965
## fail -1.102e+00 2.674e+00 -0.412 0.680350
## first 6.459e+00 3.018e+00 2.140 0.032367 *
## improv 1.003e+00 3.304e+00 0.304 0.761434
## isnt -2.307e+00 4.509e+00 -0.512 0.608949
## made -2.578e+00 3.909e+00 -0.660 0.509554
## mean 6.633e-01 4.657e+00 0.142 0.886737
## put -1.536e+00 5.656e+00 -0.272 0.785943
## yet -6.462e+00 5.903e+00 -1.095 0.273593
## next. 5.762e-01 4.720e+00 0.122 0.902848
## thank -5.940e-01 1.437e+00 -0.413 0.679354
## market 1.899e+00 2.539e+00 0.748 0.454445
## blackberri 2.786e+00 2.163e+00 1.288 0.197765
## mac 2.099e+00 3.502e+00 0.599 0.548864
## never -1.050e+01 5.678e+01 -0.185 0.853310
## plastic -6.757e+00 4.268e+00 -1.583 0.113385
## right 5.167e-01 3.213e+00 0.161 0.872238
## sell 4.129e+00 3.546e+00 1.165 0.244146
## pleas -3.411e+00 3.056e+00 -1.116 0.264311
## guy -2.946e+00 1.120e+01 -0.263 0.792498
## scanner -7.743e+00 4.906e+00 -1.578 0.114539
## .rnorm -4.078e-01 3.364e-01 -1.212 0.225376
## want 3.360e+00 1.707e+00 1.969 0.048991 *
## black -6.128e+00 3.130e+00 -1.958 0.050252 .
## free -7.147e+00 3.749e+00 -1.906 0.056628 .
## innov 1.812e+00 2.144e+00 0.845 0.398013
## via -1.910e+01 2.656e+03 -0.007 0.994263
## come -2.213e+00 2.399e+00 -0.922 0.356308
## good 2.133e-01 3.934e+00 0.054 0.956754
## let -2.949e+00 3.645e+00 -0.809 0.418587
## stop -2.276e+00 2.928e+00 -0.777 0.437016
## color -5.407e+00 3.264e+00 -1.656 0.097657 .
## compani -5.923e+00 4.523e+00 -1.310 0.190362
## know -3.860e+00 3.856e+00 -1.001 0.316843
## price -9.302e-01 3.523e+00 -0.264 0.791751
## just 1.934e-01 1.636e+00 0.118 0.905904
## store -1.528e+00 2.187e+00 -0.699 0.484820
## ios7 2.098e+00 3.101e+00 0.676 0.498724
## peopl 2.078e-01 3.104e+00 0.067 0.946625
## releas -6.632e+00 4.267e+00 -1.554 0.120110
## say -5.207e+00 3.348e+00 -1.555 0.119883
## anyon 2.952e+00 2.252e+00 1.311 0.189949
## devic -4.604e+01 1.593e+03 -0.029 0.976940
## need -3.808e+00 2.114e+00 -1.801 0.071640 .
## back -5.625e+00 3.573e+00 -1.574 0.115436
## googl -5.192e+00 3.239e+00 -1.603 0.108970
## itun 2.276e+00 1.116e+00 2.039 0.041490 *
## microsoft -3.679e+00 2.440e+00 -1.508 0.131625
## will -3.733e+00 2.198e+00 -1.698 0.089485 .
## appl -1.461e+00 3.362e+00 -0.435 0.663772
## new -3.149e+00 1.727e+00 -1.823 0.068257 .
## iphone5 8.373e-01 1.631e+00 0.513 0.607712
## ipad -2.375e+00 1.659e+00 -1.431 0.152377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 705.23 on 824 degrees of freedom
## Residual deviance: 212.78 on 616 degrees of freedom
## AIC: 630.78
##
## Number of Fisher Scoring iterations: 18
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 608 8
## Y 91 118
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 608
## 2 Y 8
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 91
## 2 118
## Reference
## Prediction N Y
## N 661 9
## Y 38 117
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 661
## 2 Y 9
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 38
## 2 117
## Reference
## Prediction N Y
## N 683 9
## Y 16 117
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 683
## 2 Y 9
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 16
## 2 117
## Reference
## Prediction N Y
## N 693 13
## Y 6 113
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 693
## 2 Y 13
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 6
## 2 113
## Reference
## Prediction N Y
## N 694 17
## Y 5 109
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 694
## 2 Y 17
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 5
## 2 109
## Reference
## Prediction N Y
## N 695 25
## Y 4 101
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 695
## 2 Y 25
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 4
## 2 101
## Reference
## Prediction N Y
## N 697 32
## Y 2 94
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 697
## 2 Y 32
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 2
## 2 94
## Reference
## Prediction N Y
## N 698 46
## Y 1 80
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 698
## 2 Y 46
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 1
## 2 80
## Reference
## Prediction N Y
## N 698 59
## Y 1 67
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 698
## 2 Y 59
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 1
## 2 67
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.7044776
## 3 0.2 0.8327402
## 4 0.3 0.9034749
## 5 0.4 0.9224490
## 6 0.5 0.9083333
## 7 0.6 0.8744589
## 8 0.7 0.8468468
## 9 0.8 0.7729469
## 10 0.9 0.6907216
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 693
## 2 Y 13
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 6
## 2 113
## Reference
## Prediction N Y
## N 693 13
## Y 6 113
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 693
## 2 Y 13
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 6
## 2 113
## Prediction
## Reference N Y
## N 693 6
## Y 13 113
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.769697e-01 9.089388e-01 9.642682e-01 9.860786e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 6.001205e-36 1.686686e-01
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 244 23
## Y 56 33
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 244
## 2 Y 23
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 56
## 2 33
## Reference
## Prediction N Y
## N 255 25
## Y 45 31
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 255
## 2 Y 25
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 45
## 2 31
## Reference
## Prediction N Y
## N 258 26
## Y 42 30
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 258
## 2 Y 26
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 42
## 2 30
## Reference
## Prediction N Y
## N 266 27
## Y 34 29
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 266
## 2 Y 27
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 34
## 2 29
## Reference
## Prediction N Y
## N 268 28
## Y 32 28
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 268
## 2 Y 28
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 32
## 2 28
## Reference
## Prediction N Y
## N 272 29
## Y 28 27
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 272
## 2 Y 29
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 28
## 2 27
## Reference
## Prediction N Y
## N 274 30
## Y 26 26
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 274
## 2 Y 30
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 26
## 2 26
## Reference
## Prediction N Y
## N 277 33
## Y 23 23
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 277
## 2 Y 33
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 23
## 2 23
## Reference
## Prediction N Y
## N 281 34
## Y 19 22
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 281
## 2 Y 34
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 19
## 2 22
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.4551724
## 3 0.2 0.4696970
## 4 0.3 0.4687500
## 5 0.4 0.4873950
## 6 0.5 0.4827586
## 7 0.6 0.4864865
## 8 0.7 0.4814815
## 9 0.8 0.4509804
## 10 0.9 0.4536082
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 266
## 2 Y 27
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 34
## 2 29
## Reference
## Prediction N Y
## N 266 27
## Y 34 29
## Negative.fctr Negative.fctr.predict.Conditional.X.glm.N
## 1 N 266
## 2 Y 27
## Negative.fctr.predict.Conditional.X.glm.Y
## 1 34
## 2 29
## Prediction
## Reference N Y
## N 266 34
## Y 27 29
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8286517 0.3849553 0.7854100 0.8663234 0.8426966
## AccuracyPValue McnemarPValue
## 0.7900786 0.4423557
## model_id model_method
## 1 Conditional.X.glm glm
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 5.138 1.152
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9641324 0.4 0.922449 0.7612121
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9642682 0.9860786 0.2321957 0.7152381
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.487395 0.8286517
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB min.aic.fit
## 1 0.78541 0.8663234 0.3849553 630.7763
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.03531831 0.08116562
## [1] "fitting model: Conditional.X.rpart"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## + Fold1: cp=0.02381
## - Fold1: cp=0.02381
## + Fold2: cp=0.02381
## - Fold2: cp=0.02381
## + Fold3: cp=0.02381
## - Fold3: cp=0.02381
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0238 on full training set
## Warning in myfit_mdl(model_id = paste0(model_id_pfx, ""), model_method =
## method, : model's bestTune found at an extreme of tuneGrid for parameter:
## cp
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.21428571 0 1.0000000
## 2 0.04761905 1 0.7857143
## 3 0.02380952 2 0.7380952
##
## Variable importance
## freak hate
## 81 19
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.275110, (0 missing)
## hate < 0.5 to the left, improve= 9.873907, (0 missing)
## stuff < 0.5 to the left, improve= 6.063648, (0 missing)
## pictur < 0.5 to the left, improve= 5.763645, (0 missing)
## wtf < 0.5 to the left, improve= 5.763645, (0 missing)
##
## Node number 2: 790 observations, complexity param=0.04761905
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
## left son=4 (780 obs) right son=5 (10 obs)
## Primary splits:
## hate < 0.5 to the left, improve=9.359591, (0 missing)
## wtf < 0.5 to the left, improve=6.410211, (0 missing)
## stuff < 0.5 to the left, improve=5.436150, (0 missing)
## fix < 0.5 to the left, improve=2.875307, (0 missing)
## cant < 0.5 to the left, improve=2.709704, (0 missing)
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## Node number 4: 780 observations
## predicted class=N expected loss=0.1115385 P(node) =0.9454545
## class counts: 693 87
## probabilities: 0.888 0.112
##
## Node number 5: 10 observations
## predicted class=Y expected loss=0.2 P(node) =0.01212121
## class counts: 2 8
## probabilities: 0.200 0.800
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532)
## 4) hate< 0.5 780 87 N (0.8884615 0.1115385) *
## 5) hate>=0.5 10 2 Y (0.2000000 0.8000000) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.4561404
## 4 0.3 0.4561404
## 5 0.4 0.4561404
## 6 0.5 0.4561404
## 7 0.6 0.4561404
## 8 0.7 0.4561404
## 9 0.8 0.4561404
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 6
## 2 39
## Prediction
## Reference N Y
## N 693 6
## Y 87 39
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.872727e-01 4.086022e-01 8.636885e-01 9.080451e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 5.582158e-04 1.080262e-16
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.2718447
## 3 0.2 0.5000000
## 4 0.3 0.5000000
## 5 0.4 0.5000000
## 6 0.5 0.5000000
## 7 0.6 0.5000000
## 8 0.7 0.5000000
## 9 0.8 0.5000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.8000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.rpart.Y
## 1 4
## 2 20
## Prediction
## Reference N Y
## N 296 4
## Y 36 20
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.876404e-01 4.478908e-01 8.501517e-01 9.185050e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 9.756283e-03 9.509294e-07
## model_id model_method
## 1 Conditional.X.rpart rpart
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 3.532 0.435
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6506404 0.8 0.4561404 0.8824242
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8636885 0.9080451 0.3699305 0.672619
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.8 0.5 0.8876404
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8501517 0.918505 0.4478908
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01469619 0.1001379
## [1] "fitting model: Conditional.X.cp.0.rpart"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.21428571 0 1.0000000
## 2 0.04761905 1 0.7857143
## 3 0.02380952 2 0.7380952
## 4 0.00000000 3 0.7142857
##
## Variable importance
## freak hate stuff
## 72 17 10
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.275110, (0 missing)
## hate < 0.5 to the left, improve= 9.873907, (0 missing)
## stuff < 0.5 to the left, improve= 6.063648, (0 missing)
## pictur < 0.5 to the left, improve= 5.763645, (0 missing)
## wtf < 0.5 to the left, improve= 5.763645, (0 missing)
##
## Node number 2: 790 observations, complexity param=0.04761905
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
## left son=4 (780 obs) right son=5 (10 obs)
## Primary splits:
## hate < 0.5 to the left, improve=9.359591, (0 missing)
## wtf < 0.5 to the left, improve=6.410211, (0 missing)
## stuff < 0.5 to the left, improve=5.436150, (0 missing)
## fix < 0.5 to the left, improve=2.875307, (0 missing)
## cant < 0.5 to the left, improve=2.709704, (0 missing)
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## Node number 4: 780 observations, complexity param=0.02380952
## predicted class=N expected loss=0.1115385 P(node) =0.9454545
## class counts: 693 87
## probabilities: 0.888 0.112
## left son=8 (771 obs) right son=9 (9 obs)
## Primary splits:
## stuff < 0.5 to the left, improve=5.611763, (0 missing)
## wtf < 0.5 to the left, improve=5.132319, (0 missing)
## fix < 0.5 to the left, improve=2.987798, (0 missing)
## cant < 0.5 to the left, improve=2.865713, (0 missing)
## better < 0.5 to the left, improve=2.865713, (0 missing)
##
## Node number 5: 10 observations
## predicted class=Y expected loss=0.2 P(node) =0.01212121
## class counts: 2 8
## probabilities: 0.200 0.800
##
## Node number 8: 771 observations
## predicted class=N expected loss=0.1050584 P(node) =0.9345455
## class counts: 690 81
## probabilities: 0.895 0.105
##
## Node number 9: 9 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.01090909
## class counts: 3 6
## probabilities: 0.333 0.667
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532)
## 4) hate< 0.5 780 87 N (0.8884615 0.1115385)
## 8) stuff< 0.5 771 81 N (0.8949416 0.1050584) *
## 9) stuff>=0.5 9 3 Y (0.3333333 0.6666667) *
## 5) hate>=0.5 10 2 Y (0.2000000 0.8000000) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.5000000
## 4 0.3 0.5000000
## 5 0.4 0.5000000
## 6 0.5 0.5000000
## 7 0.6 0.5000000
## 8 0.7 0.4561404
## 9 0.8 0.4561404
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 9
## 2 45
## Prediction
## Reference N Y
## N 690 9
## Y 81 45
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.909091e-01 4.495596e-01 8.676171e-01 9.113614e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 1.744490e-04 7.206261e-14
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 296 36
## Y 4 20
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 36
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 20
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 300 56
## Y 0 0
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 300
## 2 Y 56
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.2718447
## 3 0.2 0.5365854
## 4 0.3 0.5365854
## 5 0.4 0.5365854
## 6 0.5 0.5365854
## 7 0.6 0.5365854
## 8 0.7 0.5000000
## 9 0.8 0.5000000
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Reference
## Prediction N Y
## N 296 34
## Y 4 22
## Negative.fctr Negative.fctr.predict.Conditional.X.cp.0.rpart.N
## 1 N 296
## 2 Y 34
## Negative.fctr.predict.Conditional.X.cp.0.rpart.Y
## 1 4
## 2 22
## Prediction
## Reference N Y
## N 296 4
## Y 34 22
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.932584e-01 4.852359e-01 8.564403e-01 9.233486e-01 8.426966e-01
## AccuracyPValue McnemarPValue
## 3.956222e-03 2.545872e-06
## model_id model_method
## 1 Conditional.X.cp.0.rpart rpart
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.932 0.429
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6727638 0.6 0.5 0.8909091
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8676171 0.9113614 0.4495596 0.6902381
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.6 0.5365854 0.8932584
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8564403 0.9233486 0.4852359
## [1] "fitting model: Conditional.X.rf"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## + : mtry= 2
## - : mtry= 2
## + : mtry=105
## - : mtry=105
## + : mtry=209
## - : mtry=209
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 105 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 825 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1650 matrix numeric
## oob.times 825 -none- numeric
## classes 2 -none- character
## importance 209 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 825 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 209 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 617 0
## Y 82 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 617
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 82
## 2 126
## Reference
## Prediction N Y
## N 676 0
## Y 23 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 676
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 23
## 2 126
## Reference
## Prediction N Y
## N 689 0
## Y 10 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 689
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 10
## 2 126
## Reference
## Prediction N Y
## N 697 0
## Y 2 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 697
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 2
## 2 126
## Reference
## Prediction N Y
## N 699 0
## Y 0 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 126
## Reference
## Prediction N Y
## N 699 1
## Y 0 125
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 1
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 125
## Reference
## Prediction N Y
## N 699 52
## Y 0 74
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 52
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 74
## Reference
## Prediction N Y
## N 699 67
## Y 0 59
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 67
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 59
## Reference
## Prediction N Y
## N 699 88
## Y 0 38
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 88
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 38
## Reference
## Prediction N Y
## N 699 116
## Y 0 10
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 116
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 10
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.7544910
## 3 0.2 0.9163636
## 4 0.3 0.9618321
## 5 0.4 0.9921260
## 6 0.5 1.0000000
## 7 0.6 0.9960159
## 8 0.7 0.7400000
## 9 0.8 0.6378378
## 10 0.9 0.4634146
## 11 1.0 0.1470588
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y NA
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 NA
## 2 126
## Reference
## Prediction N Y
## N 699 0
## Y 0 126
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 699
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 0
## 2 126
## Prediction
## Reference N Y
## N 699 0
## Y 0 126
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 1.000000e+00 1.000000e+00 9.955386e-01 1.000000e+00 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.160662e-60 NaN
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 224 18
## Y 76 38
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 224
## 2 Y 18
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 76
## 2 38
## Reference
## Prediction N Y
## N 252 21
## Y 48 35
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 252
## 2 Y 21
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 48
## 2 35
## Reference
## Prediction N Y
## N 265 24
## Y 35 32
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 265
## 2 Y 24
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 35
## 2 32
## Reference
## Prediction N Y
## N 275 26
## Y 25 30
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 275
## 2 Y 26
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 282 29
## Y 18 27
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 282
## 2 Y 29
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 18
## 2 27
## Reference
## Prediction N Y
## N 288 31
## Y 12 25
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 288
## 2 Y 31
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 12
## 2 25
## Reference
## Prediction N Y
## N 292 33
## Y 8 23
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 292
## 2 Y 33
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 8
## 2 23
## Reference
## Prediction N Y
## N 296 38
## Y 4 18
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 296
## 2 Y 38
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 4
## 2 18
## Reference
## Prediction N Y
## N 297 43
## Y 3 13
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 297
## 2 Y 43
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 3
## 2 13
## Reference
## Prediction N Y
## N 299 52
## Y 1 4
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 299
## 2 Y 52
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 1
## 2 4
## threshold f.score
## 1 0.0 0.2718447
## 2 0.1 0.4470588
## 3 0.2 0.5035971
## 4 0.3 0.5203252
## 5 0.4 0.5405405
## 6 0.5 0.5346535
## 7 0.6 0.5376344
## 8 0.7 0.5287356
## 9 0.8 0.4615385
## 10 0.9 0.3611111
## 11 1.0 0.1311475
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 275
## 2 Y 26
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Reference
## Prediction N Y
## N 275 26
## Y 25 30
## Negative.fctr Negative.fctr.predict.Conditional.X.rf.N
## 1 N 275
## 2 Y 26
## Negative.fctr.predict.Conditional.X.rf.Y
## 1 25
## 2 30
## Prediction
## Reference N Y
## N 275 25
## Y 26 30
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8567416 0.4556901 0.8159786 0.8914388 0.8426966
## AccuracyPValue McnemarPValue
## 0.2593176 1.0000000
## model_id model_method
## 1 Conditional.X.rf rf
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 91.638 23.538
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 1 0.5 1 0.88
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9955386 1 0.4526428 0.7953274
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.5405405 0.8567416
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8159786 0.8914388 0.4556901
## [1] "fitting model: Conditional.X.no.rnorm.rf"
## [1] " indep_vars: freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## + : mtry= 2
## - : mtry= 2
## + : mtry=105
## - : mtry=105
## + : mtry=208
## - : mtry=208
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 208 on full training set
## Warning in myfit_mdl(model_id = paste0(model_id_pfx, ".no.rnorm"),
## model_method = method, : model's bestTune found at an extreme of tuneGrid
## for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 825 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 1650 matrix numeric
## oob.times 825 -none- numeric
## classes 2 -none- character
## importance 208 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 825 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 208 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 617 6
## Y 82 120
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 617
## 2 Y 6
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 82
## 2 120
## Reference
## Prediction N Y
## N 664 6
## Y 35 120
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 664
## 2 Y 6
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 35
## 2 120
## Reference
## Prediction N Y
## N 685 6
## Y 14 120
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 685
## 2 Y 6
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 14
## 2 120
## Reference
## Prediction N Y
## N 697 7
## Y 2 119
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 697
## 2 Y 7
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 2
## 2 119
## Reference
## Prediction N Y
## N 697 7
## Y 2 119
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 697
## 2 Y 7
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 2
## 2 119
## Reference
## Prediction N Y
## N 698 9
## Y 1 117
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 698
## 2 Y 9
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 117
## Reference
## Prediction N Y
## N 698 52
## Y 1 74
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 698
## 2 Y 52
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 74
## Reference
## Prediction N Y
## N 698 65
## Y 1 61
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 698
## 2 Y 65
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 61
## Reference
## Prediction N Y
## N 698 89
## Y 1 37
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 698
## 2 Y 89
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 1
## 2 37
## Reference
## Prediction N Y
## N 699 120
## Y 0 6
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 699
## 2 Y 120
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 6
## threshold f.score
## 1 0.0 0.26498423
## 2 0.1 0.73170732
## 3 0.2 0.85409253
## 4 0.3 0.92307692
## 5 0.4 0.96356275
## 6 0.5 0.96356275
## 7 0.6 0.95901639
## 8 0.7 0.73631841
## 9 0.8 0.64893617
## 10 0.9 0.45121951
## 11 1.0 0.09090909
## [1] "Classifier Probability Threshold: 0.5000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 697
## 2 Y 7
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 2
## 2 119
## Reference
## Prediction N Y
## N 697 7
## Y 2 119
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 697
## 2 Y 7
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 2
## 2 119
## Prediction
## Reference N Y
## N 697 2
## Y 7 119
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 9.890909e-01 9.571510e-01 9.793925e-01 9.949999e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 4.156156e-46 1.824224e-01
## Reference
## Prediction N Y
## N 0 0
## Y 300 56
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 300
## 2 56
## Reference
## Prediction N Y
## N 238 18
## Y 62 38
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 238
## 2 Y 18
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 62
## 2 38
## Reference
## Prediction N Y
## N 266 20
## Y 34 36
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 266
## 2 Y 20
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 34
## 2 36
## Reference
## Prediction N Y
## N 269 23
## Y 31 33
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 269
## 2 Y 23
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 31
## 2 33
## Reference
## Prediction N Y
## N 275 23
## Y 25 33
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 275
## 2 Y 23
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 33
## Reference
## Prediction N Y
## N 280 26
## Y 20 30
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 280
## 2 Y 26
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 20
## 2 30
## Reference
## Prediction N Y
## N 291 31
## Y 9 25
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 291
## 2 Y 31
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 9
## 2 25
## Reference
## Prediction N Y
## N 292 32
## Y 8 24
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 292
## 2 Y 32
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 8
## 2 24
## Reference
## Prediction N Y
## N 295 37
## Y 5 19
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 295
## 2 Y 37
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 5
## 2 19
## Reference
## Prediction N Y
## N 297 37
## Y 3 19
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 297
## 2 Y 37
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 3
## 2 19
## Reference
## Prediction N Y
## N 300 55
## Y 0 1
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 300
## 2 Y 55
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 0
## 2 1
## threshold f.score
## 1 0.0 0.27184466
## 2 0.1 0.48717949
## 3 0.2 0.57142857
## 4 0.3 0.55000000
## 5 0.4 0.57894737
## 6 0.5 0.56603774
## 7 0.6 0.55555556
## 8 0.7 0.54545455
## 9 0.8 0.47500000
## 10 0.9 0.48717949
## 11 1.0 0.03508772
## [1] "Classifier Probability Threshold: 0.4000 to maximize f.score.OOB"
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 275
## 2 Y 23
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 33
## Reference
## Prediction N Y
## N 275 23
## Y 25 33
## Negative.fctr Negative.fctr.predict.Conditional.X.no.rnorm.rf.N
## 1 N 275
## 2 Y 23
## Negative.fctr.predict.Conditional.X.no.rnorm.rf.Y
## 1 25
## 2 33
## Prediction
## Reference N Y
## N 275 25
## Y 23 33
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.8651685 0.4987092 0.8252354 0.8988853 0.8426966
## AccuracyPValue McnemarPValue
## 0.1366213 0.8852339
## model_id model_method
## 1 Conditional.X.no.rnorm.rf rf
## feats
## 1 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 126.053 43.174
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.9619184 0.5 0.9635628 0.8715152
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.9793925 0.9949999 0.4376929 0.81125
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.4 0.5789474 0.8651685
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8252354 0.8988853 0.4987092
# User specified
# easier to exclude features
#model_id_pfx <- "";
# indep_vars_vctr <- setdiff(names(glb_trnent_df),
# union(union(glb_rsp_var, glb_exclude_vars_as_features),
# c("<feat1_name>", "<feat2_name>")))
# method <- ""
# easier to include features
#model_id_pfx <- ""; indep_vars_vctr <- c("<feat1_name>", "<feat1_name>"); method <- ""
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_trnent_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_trnent_df, OOB_df=glb_newent_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_trnent_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_trnent_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)
## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.y.glm glm
## 8 Low.cor.X.glm glm
## 9 Conditional.X.glm glm
## 10 Conditional.X.rpart rpart
## 11 Conditional.X.cp.0.rpart rpart
## 12 Conditional.X.rf rf
## 13 Conditional.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 freak
## 4 freak
## 5 freak
## 6 freak
## 7 freak, freak:cdp, freak:httpbitly18xc8dk, freak:femal, freak:refus, freak:emiss, freak:itun
## 8 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, yall, break., imessag, stand, togeth, cheap, wont, make, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5
## 9 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 10 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 11 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 12 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 13 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.304 0.002
## 2 0 0.226 0.001
## 3 0 0.590 0.015
## 4 0 0.449 0.013
## 5 3 0.956 0.013
## 6 1 1.091 0.015
## 7 1 0.928 0.033
## 8 1 4.411 1.087
## 9 1 5.138 1.152
## 10 3 3.532 0.435
## 11 0 0.932 0.429
## 12 3 91.638 23.538
## 13 3 126.053 43.174
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5000000 0.5 0.0000000 0.8472727
## 2 0.4876695 0.1 0.2649842 0.1527273
## 3 0.5000000 0.5 0.0000000 0.8472727
## 4 0.6201546 0.8 0.3850932 0.8800000
## 5 0.6201546 0.8 0.3850932 0.8800000
## 6 0.6202001 0.8 0.3850932 0.8800000
## 7 0.6202455 0.8 0.3850932 0.8800000
## 8 0.9634853 0.4 0.9180328 0.7503030
## 9 0.9641324 0.4 0.9224490 0.7612121
## 10 0.6506404 0.8 0.4561404 0.8824242
## 11 0.6727638 0.6 0.5000000 0.8909091
## 12 1.0000000 0.5 1.0000000 0.8800000
## 13 0.9619184 0.5 0.9635628 0.8715152
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit max.auc.OOB
## 1 0.8208848 0.8711498 0.0000000 0.5000000
## 2 0.1288502 0.1791152 0.0000000 0.5047619
## 3 0.8208848 0.8711498 0.0000000 0.5000000
## 4 0.8558521 0.9013914 0.3413572 0.6395238
## 5 0.8558521 0.9013914 0.3378153 0.6395238
## 6 0.8558521 0.9013914 0.3378153 0.6395833
## 7 0.8558521 0.9013914 0.3378153 0.6396429
## 8 0.9628069 0.9851307 0.2316521 0.7086607
## 9 0.9642682 0.9860786 0.2321957 0.7152381
## 10 0.8636885 0.9080451 0.3699305 0.6726190
## 11 0.8676171 0.9113614 0.4495596 0.6902381
## 12 0.9955386 1.0000000 0.4526428 0.7953274
## 13 0.9793925 0.9949999 0.4376929 0.8112500
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.0000000 0.8426966
## 2 0.1 0.2718447 0.1573034
## 3 0.5 0.0000000 0.8426966
## 4 0.8 0.4324324 0.8820225
## 5 0.8 0.4324324 0.8820225
## 6 0.8 0.4324324 0.8820225
## 7 0.8 0.4324324 0.8820225
## 8 0.3 0.4960000 0.8230337
## 9 0.4 0.4873950 0.8286517
## 10 0.8 0.5000000 0.8876404
## 11 0.6 0.5365854 0.8932584
## 12 0.4 0.5405405 0.8567416
## 13 0.4 0.5789474 0.8651685
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.8006423 0.8789342 0.0000000
## 2 0.1210658 0.1993577 0.0000000
## 3 0.8006423 0.8789342 0.0000000
## 4 0.8438883 0.9136353 0.3853995
## 5 0.8438883 0.9136353 0.3853995
## 6 0.8438883 0.9136353 0.3853995
## 7 0.8438883 0.9136353 0.3853995
## 8 0.7793438 0.8612518 0.3900794
## 9 0.7854100 0.8663234 0.3849553
## 10 0.8501517 0.9185050 0.4478908
## 11 0.8564403 0.9233486 0.4852359
## 12 0.8159786 0.8914388 0.4556901
## 13 0.8252354 0.8988853 0.4987092
## max.AccuracySD.fit max.KappaSD.fit min.aic.fit
## 1 NA NA NA
## 2 NA NA NA
## 3 NA NA NA
## 4 NA NA NA
## 5 0.01585054 0.11932477 NA
## 6 0.01585054 0.11932477 608.9243
## 7 0.01585054 0.11932477 610.3916
## 8 0.04963781 0.09400829 623.1343
## 9 0.03531831 0.08116562 630.7763
## 10 0.01469619 0.10013786 NA
## 11 NA NA NA
## 12 NA NA NA
## 13 NA NA NA
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.models",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed8 fit.models 5 0 24.668
## elapsed9 fit.models 5 1 299.425
if (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_trnent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_newent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
## model_id model_method
## 1 MFO.myMFO_classfr myMFO_classfr
## 2 Random.myrandom_classfr myrandom_classfr
## 3 Max.cor.Y.cv.0.rpart rpart
## 4 Max.cor.Y.cv.0.cp.0.rpart rpart
## 5 Max.cor.Y.rpart rpart
## 6 Max.cor.Y.glm glm
## 7 Interact.High.cor.y.glm glm
## 8 Low.cor.X.glm glm
## 9 Conditional.X.glm glm
## 10 Conditional.X.rpart rpart
## 11 Conditional.X.cp.0.rpart rpart
## 12 Conditional.X.rf rf
## 13 Conditional.X.no.rnorm.rf rf
## feats
## 1 .rnorm
## 2 .rnorm
## 3 freak
## 4 freak
## 5 freak
## 6 freak
## 7 freak, freak:cdp, freak:httpbitly18xc8dk, freak:femal, freak:refus, freak:emiss, freak:itun
## 8 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, yall, break., imessag, stand, togeth, cheap, wont, make, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5
## 9 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 10 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 11 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 12 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, .rnorm, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## 13 freak, hate, stuff, pictur, wtf, cant, shame, stupid, even, line, yooo, better, ever, fix, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns max.auc.fit opt.prob.threshold.fit max.f.score.fit
## 1 0 0.5000000 0.5 0.0000000
## 2 0 0.4876695 0.1 0.2649842
## 3 0 0.5000000 0.5 0.0000000
## 4 0 0.6201546 0.8 0.3850932
## 5 3 0.6201546 0.8 0.3850932
## 6 1 0.6202001 0.8 0.3850932
## 7 1 0.6202455 0.8 0.3850932
## 8 1 0.9634853 0.4 0.9180328
## 9 1 0.9641324 0.4 0.9224490
## 10 3 0.6506404 0.8 0.4561404
## 11 0 0.6727638 0.6 0.5000000
## 12 3 1.0000000 0.5 1.0000000
## 13 3 0.9619184 0.5 0.9635628
## max.Accuracy.fit max.Kappa.fit max.auc.OOB opt.prob.threshold.OOB
## 1 0.8472727 0.0000000 0.5000000 0.5
## 2 0.1527273 0.0000000 0.5047619 0.1
## 3 0.8472727 0.0000000 0.5000000 0.5
## 4 0.8800000 0.3413572 0.6395238 0.8
## 5 0.8800000 0.3378153 0.6395238 0.8
## 6 0.8800000 0.3378153 0.6395833 0.8
## 7 0.8800000 0.3378153 0.6396429 0.8
## 8 0.7503030 0.2316521 0.7086607 0.3
## 9 0.7612121 0.2321957 0.7152381 0.4
## 10 0.8824242 0.3699305 0.6726190 0.8
## 11 0.8909091 0.4495596 0.6902381 0.6
## 12 0.8800000 0.4526428 0.7953274 0.4
## 13 0.8715152 0.4376929 0.8112500 0.4
## max.f.score.OOB max.Accuracy.OOB max.Kappa.OOB
## 1 0.0000000 0.8426966 0.0000000
## 2 0.2718447 0.1573034 0.0000000
## 3 0.0000000 0.8426966 0.0000000
## 4 0.4324324 0.8820225 0.3853995
## 5 0.4324324 0.8820225 0.3853995
## 6 0.4324324 0.8820225 0.3853995
## 7 0.4324324 0.8820225 0.3853995
## 8 0.4960000 0.8230337 0.3900794
## 9 0.4873950 0.8286517 0.3849553
## 10 0.5000000 0.8876404 0.4478908
## 11 0.5365854 0.8932584 0.4852359
## 12 0.5405405 0.8567416 0.4556901
## 13 0.5789474 0.8651685 0.4987092
## inv.elapsedtime.everything inv.elapsedtime.final inv.aic.fit
## 1 3.289473684 5.000000e+02 NA
## 2 4.424778761 1.000000e+03 NA
## 3 1.694915254 6.666667e+01 NA
## 4 2.227171492 7.692308e+01 NA
## 5 1.046025105 7.692308e+01 NA
## 6 0.916590284 6.666667e+01 0.001642240
## 7 1.077586207 3.030303e+01 0.001638293
## 8 0.226705962 9.199632e-01 0.001604790
## 9 0.194628260 8.680556e-01 0.001585348
## 10 0.283125708 2.298851e+00 NA
## 11 1.072961373 2.331002e+00 NA
## 12 0.010912504 4.248449e-02 NA
## 13 0.007933171 2.316209e-02 NA
print(myplot_radar(radar_inp_df=plt_models_df))
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
## Warning: Removed 6 rows containing missing values (geom_path).
## Warning: Removed 104 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_text).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <- unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data, sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")], all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
dev.off()
## quartz_off_screen
## 2
print(gp)
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
print(tmp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, c("model_id", glb_model_evl_criteria)])
## model_id max.Accuracy.OOB max.Kappa.OOB min.aic.fit
## 11 Conditional.X.cp.0.rpart 0.8932584 0.4852359 NA
## 10 Conditional.X.rpart 0.8876404 0.4478908 NA
## 6 Max.cor.Y.glm 0.8820225 0.3853995 608.9243
## 7 Interact.High.cor.y.glm 0.8820225 0.3853995 610.3916
## 4 Max.cor.Y.cv.0.cp.0.rpart 0.8820225 0.3853995 NA
## 5 Max.cor.Y.rpart 0.8820225 0.3853995 NA
## 13 Conditional.X.no.rnorm.rf 0.8651685 0.4987092 NA
## 12 Conditional.X.rf 0.8567416 0.4556901 NA
## 1 MFO.myMFO_classfr 0.8426966 0.0000000 NA
## 3 Max.cor.Y.cv.0.rpart 0.8426966 0.0000000 NA
## 9 Conditional.X.glm 0.8286517 0.3849553 630.7763
## 8 Low.cor.X.glm 0.8230337 0.3900794 623.1343
## 2 Random.myrandom_classfr 0.1573034 0.0000000 NA
print(myplot_radar(radar_inp_df=tmp_models_df))
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
## Warning: Removed 6 rows containing missing values (geom_path).
## Warning: Removed 27 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_text).
## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors
## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 13. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.Kappa.OOB + min.aic.fit
print(sprintf("Best model id: %s", tmp_models_df[1, "model_id"]))
## [1] "Best model id: Conditional.X.cp.0.rpart"
if (is.null(glb_sel_mdl_id))
{ glb_sel_mdl_id <- tmp_models_df[1, "model_id"] } else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.21428571 0 1.0000000
## 2 0.04761905 1 0.7857143
## 3 0.02380952 2 0.7380952
## 4 0.00000000 3 0.7142857
##
## Variable importance
## freak hate stuff
## 72 17 10
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.275110, (0 missing)
## hate < 0.5 to the left, improve= 9.873907, (0 missing)
## stuff < 0.5 to the left, improve= 6.063648, (0 missing)
## pictur < 0.5 to the left, improve= 5.763645, (0 missing)
## wtf < 0.5 to the left, improve= 5.763645, (0 missing)
##
## Node number 2: 790 observations, complexity param=0.04761905
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
## left son=4 (780 obs) right son=5 (10 obs)
## Primary splits:
## hate < 0.5 to the left, improve=9.359591, (0 missing)
## wtf < 0.5 to the left, improve=6.410211, (0 missing)
## stuff < 0.5 to the left, improve=5.436150, (0 missing)
## fix < 0.5 to the left, improve=2.875307, (0 missing)
## cant < 0.5 to the left, improve=2.709704, (0 missing)
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## Node number 4: 780 observations, complexity param=0.02380952
## predicted class=N expected loss=0.1115385 P(node) =0.9454545
## class counts: 693 87
## probabilities: 0.888 0.112
## left son=8 (771 obs) right son=9 (9 obs)
## Primary splits:
## stuff < 0.5 to the left, improve=5.611763, (0 missing)
## wtf < 0.5 to the left, improve=5.132319, (0 missing)
## fix < 0.5 to the left, improve=2.987798, (0 missing)
## cant < 0.5 to the left, improve=2.865713, (0 missing)
## better < 0.5 to the left, improve=2.865713, (0 missing)
##
## Node number 5: 10 observations
## predicted class=Y expected loss=0.2 P(node) =0.01212121
## class counts: 2 8
## probabilities: 0.200 0.800
##
## Node number 8: 771 observations
## predicted class=N expected loss=0.1050584 P(node) =0.9345455
## class counts: 690 81
## probabilities: 0.895 0.105
##
## Node number 9: 9 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.01090909
## class counts: 3 6
## probabilities: 0.333 0.667
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532)
## 4) hate< 0.5 780 87 N (0.8884615 0.1115385)
## 8) stuff< 0.5 771 81 N (0.8949416 0.1050584) *
## 9) stuff>=0.5 9 3 Y (0.3333333 0.6666667) *
## 5) hate>=0.5 10 2 Y (0.2000000 0.8000000) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## [1] TRUE
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed9 fit.models 5 1 299.425
## elapsed10 fit.data.training.all 6 0 313.567
6: fit.data.training.allif (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl, entity_df=glb_trnent_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=mdl_feats_df$id, model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=NULL,
n_cv_folds=glb_n_cv_folds, tune_models_df=tune_finmdl_df,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}
## importance id fit.feat
## freak 100.000000 freak TRUE
## hate 48.971207 hate TRUE
## wtf 44.063971 wtf TRUE
## stuff 43.568454 stuff TRUE
## fix 14.928296 fix TRUE
## pictur 14.675057 pictur TRUE
## cant 14.195802 cant TRUE
## better 7.296511 better TRUE
## shame 0.000000 shame TRUE
## stupid 0.000000 stupid TRUE
## even 0.000000 even TRUE
## line 0.000000 line TRUE
## yooo 0.000000 yooo TRUE
## ever 0.000000 ever TRUE
## charger 0.000000 charger TRUE
## still 0.000000 still TRUE
## charg 0.000000 charg TRUE
## disappoint 0.000000 disappoint TRUE
## short 0.000000 short TRUE
## like 0.000000 like TRUE
## amazon 0.000000 amazon TRUE
## yall 0.000000 yall TRUE
## break. 0.000000 break. TRUE
## imessag 0.000000 imessag TRUE
## stand 0.000000 stand TRUE
## togeth 0.000000 togeth TRUE
## cheap 0.000000 cheap TRUE
## wont 0.000000 wont TRUE
## make 0.000000 make TRUE
## carbon 0.000000 carbon TRUE
## darn 0.000000 darn TRUE
## httpbitly18xc8dk 0.000000 httpbitly18xc8dk TRUE
## dear 0.000000 dear TRUE
## facebook 0.000000 facebook TRUE
## X7evenstarz 0.000000 X7evenstarz TRUE
## condom 0.000000 condom TRUE
## femal 0.000000 femal TRUE
## money 0.000000 money TRUE
## theyr 0.000000 theyr TRUE
## batteri 0.000000 batteri TRUE
## china 0.000000 china TRUE
## turn 0.000000 turn TRUE
## hope 0.000000 hope TRUE
## life 0.000000 life TRUE
## sinc 0.000000 sinc TRUE
## steve 0.000000 steve TRUE
## switch 0.000000 switch TRUE
## everi 0.000000 everi TRUE
## last 0.000000 last TRUE
## your 0.000000 your TRUE
## amaz 0.000000 amaz TRUE
## arent 0.000000 arent TRUE
## date 0.000000 date TRUE
## divulg 0.000000 divulg TRUE
## ill 0.000000 ill TRUE
## ive 0.000000 ive TRUE
## lost 0.000000 lost TRUE
## noth 0.000000 noth TRUE
## worst 0.000000 worst TRUE
## phone 0.000000 phone TRUE
## care 0.000000 care TRUE
## way 0.000000 way TRUE
## year 0.000000 year TRUE
## updat 0.000000 updat TRUE
## app 0.000000 app TRUE
## iphon 0.000000 iphon TRUE
## data 0.000000 data TRUE
## take 0.000000 take TRUE
## card 0.000000 card TRUE
## custom 0.000000 custom TRUE
## die 0.000000 die TRUE
## event 0.000000 event TRUE
## problem 0.000000 problem TRUE
## refus 0.000000 refus TRUE
## two 0.000000 two TRUE
## use 0.000000 use TRUE
## screen 0.000000 screen TRUE
## thing 0.000000 thing TRUE
## buy 0.000000 buy TRUE
## tri 0.000000 tri TRUE
## cdp 0.000000 cdp TRUE
## doesnt 0.000000 doesnt TRUE
## emiss 0.000000 emiss TRUE
## feel 0.000000 feel TRUE
## fun 0.000000 fun TRUE
## got 0.000000 got TRUE
## hour 0.000000 hour TRUE
## macbook 0.000000 macbook TRUE
## miss 0.000000 miss TRUE
## siri 0.000000 siri TRUE
## start 0.000000 start TRUE
## upgrad 0.000000 upgrad TRUE
## get 0.000000 get TRUE
## think 0.000000 think TRUE
## copi 0.000000 copi TRUE
## guess 0.000000 guess TRUE
## person 0.000000 person TRUE
## smart 0.000000 smart TRUE
## best 0.000000 best TRUE
## differ 0.000000 differ TRUE
## nsa 0.000000 nsa TRUE
## product 0.000000 product TRUE
## twitter 0.000000 twitter TRUE
## old 0.000000 old TRUE
## see 0.000000 see TRUE
## download 0.000000 download TRUE
## idea 0.000000 idea TRUE
## job 0.000000 job TRUE
## mani 0.000000 mani TRUE
## simpl 0.000000 simpl TRUE
## soon 0.000000 soon TRUE
## team 0.000000 team TRUE
## technolog 0.000000 technolog TRUE
## wow 0.000000 wow TRUE
## much 0.000000 much TRUE
## support 0.000000 support TRUE
## text 0.000000 text TRUE
## time 0.000000 time TRUE
## android 0.000000 android TRUE
## that 0.000000 that TRUE
## fingerprint 0.000000 fingerprint TRUE
## now 0.000000 now TRUE
## ask 0.000000 ask TRUE
## colour 0.000000 colour TRUE
## gonna 0.000000 gonna TRUE
## happen 0.000000 happen TRUE
## man 0.000000 man TRUE
## give 0.000000 give TRUE
## hey 0.000000 hey TRUE
## today 0.000000 today TRUE
## case 0.000000 case TRUE
## chang 0.000000 chang TRUE
## one 0.000000 one TRUE
## work 0.000000 work TRUE
## day 0.000000 day TRUE
## nokia 0.000000 nokia TRUE
## preorder 0.000000 preorder TRUE
## look 0.000000 look TRUE
## awesom 0.000000 awesom TRUE
## done 0.000000 done TRUE
## featur 0.000000 featur TRUE
## said 0.000000 said TRUE
## secur 0.000000 secur TRUE
## seem 0.000000 seem TRUE
## smartphon 0.000000 smartphon TRUE
## thought 0.000000 thought TRUE
## what 0.000000 what TRUE
## windowsphon 0.000000 windowsphon TRUE
## bit 0.000000 bit TRUE
## realli 0.000000 realli TRUE
## can 0.000000 can TRUE
## sure 0.000000 sure TRUE
## dont 0.000000 dont TRUE
## impress 0.000000 impress TRUE
## call 0.000000 call TRUE
## didnt 0.000000 didnt TRUE
## drop 0.000000 drop TRUE
## fail 0.000000 fail TRUE
## first 0.000000 first TRUE
## improv 0.000000 improv TRUE
## isnt 0.000000 isnt TRUE
## made 0.000000 made TRUE
## mean 0.000000 mean TRUE
## put 0.000000 put TRUE
## yet 0.000000 yet TRUE
## next. 0.000000 next. TRUE
## thank 0.000000 thank TRUE
## market 0.000000 market TRUE
## blackberri 0.000000 blackberri TRUE
## mac 0.000000 mac TRUE
## never 0.000000 never TRUE
## plastic 0.000000 plastic TRUE
## right 0.000000 right TRUE
## sell 0.000000 sell TRUE
## pleas 0.000000 pleas TRUE
## guy 0.000000 guy TRUE
## scanner 0.000000 scanner TRUE
## want 0.000000 want TRUE
## black 0.000000 black TRUE
## free 0.000000 free TRUE
## innov 0.000000 innov TRUE
## via 0.000000 via TRUE
## come 0.000000 come TRUE
## good 0.000000 good TRUE
## let 0.000000 let TRUE
## stop 0.000000 stop TRUE
## color 0.000000 color TRUE
## compani 0.000000 compani TRUE
## know 0.000000 know TRUE
## price 0.000000 price TRUE
## just 0.000000 just TRUE
## store 0.000000 store TRUE
## ios7 0.000000 ios7 TRUE
## peopl 0.000000 peopl TRUE
## releas 0.000000 releas TRUE
## say 0.000000 say TRUE
## anyon 0.000000 anyon TRUE
## devic 0.000000 devic TRUE
## need 0.000000 need TRUE
## back 0.000000 back TRUE
## googl 0.000000 googl TRUE
## itun 0.000000 itun TRUE
## microsoft 0.000000 microsoft TRUE
## will 0.000000 will TRUE
## appl 0.000000 appl TRUE
## new 0.000000 new TRUE
## iphone5 0.000000 iphone5 TRUE
## ipad 0.000000 ipad TRUE
## [1] "fitting model: Final.rpart"
## [1] " indep_vars: freak, hate, wtf, stuff, fix, pictur, cant, better, shame, stupid, even, line, yooo, ever, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad"
## + Fold1: cp=0
## - Fold1: cp=0
## + Fold2: cp=0
## - Fold2: cp=0
## + Fold3: cp=0
## - Fold3: cp=0
## Aggregating results
## Fitting final model on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 825
##
## CP nsplit rel error
## 1 0.21428571 0 1.0000000
## 2 0.04761905 1 0.7857143
## 3 0.02380952 2 0.7380952
## 4 0.00000000 3 0.7142857
##
## Variable importance
## freak hate stuff
## 72 17 10
##
## Node number 1: 825 observations, complexity param=0.2142857
## predicted class=N expected loss=0.1527273 P(node) =1
## class counts: 699 126
## probabilities: 0.847 0.153
## left son=2 (790 obs) right son=3 (35 obs)
## Primary splits:
## freak < 0.5 to the left, improve=39.275110, (0 missing)
## hate < 0.5 to the left, improve= 9.873907, (0 missing)
## stuff < 0.5 to the left, improve= 6.063648, (0 missing)
## wtf < 0.5 to the left, improve= 5.763645, (0 missing)
## pictur < 0.5 to the left, improve= 5.763645, (0 missing)
##
## Node number 2: 790 observations, complexity param=0.04761905
## predicted class=N expected loss=0.1202532 P(node) =0.9575758
## class counts: 695 95
## probabilities: 0.880 0.120
## left son=4 (780 obs) right son=5 (10 obs)
## Primary splits:
## hate < 0.5 to the left, improve=9.359591, (0 missing)
## wtf < 0.5 to the left, improve=6.410211, (0 missing)
## stuff < 0.5 to the left, improve=5.436150, (0 missing)
## fix < 0.5 to the left, improve=2.875307, (0 missing)
## cant < 0.5 to the left, improve=2.709704, (0 missing)
##
## Node number 3: 35 observations
## predicted class=Y expected loss=0.1142857 P(node) =0.04242424
## class counts: 4 31
## probabilities: 0.114 0.886
##
## Node number 4: 780 observations, complexity param=0.02380952
## predicted class=N expected loss=0.1115385 P(node) =0.9454545
## class counts: 693 87
## probabilities: 0.888 0.112
## left son=8 (771 obs) right son=9 (9 obs)
## Primary splits:
## stuff < 0.5 to the left, improve=5.611763, (0 missing)
## wtf < 0.5 to the left, improve=5.132319, (0 missing)
## fix < 0.5 to the left, improve=2.987798, (0 missing)
## cant < 0.5 to the left, improve=2.865713, (0 missing)
## better < 0.5 to the left, improve=2.865713, (0 missing)
##
## Node number 5: 10 observations
## predicted class=Y expected loss=0.2 P(node) =0.01212121
## class counts: 2 8
## probabilities: 0.200 0.800
##
## Node number 8: 771 observations
## predicted class=N expected loss=0.1050584 P(node) =0.9345455
## class counts: 690 81
## probabilities: 0.895 0.105
##
## Node number 9: 9 observations
## predicted class=Y expected loss=0.3333333 P(node) =0.01090909
## class counts: 3 6
## probabilities: 0.333 0.667
##
## n= 825
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 825 126 N (0.8472727 0.1527273)
## 2) freak< 0.5 790 95 N (0.8797468 0.1202532)
## 4) hate< 0.5 780 87 N (0.8884615 0.1115385)
## 8) stuff< 0.5 771 81 N (0.8949416 0.1050584) *
## 9) stuff>=0.5 9 3 Y (0.3333333 0.6666667) *
## 5) hate>=0.5 10 2 Y (0.2000000 0.8000000) *
## 3) freak>=0.5 35 4 Y (0.1142857 0.8857143) *
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Final.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 0 0
## Y 699 126
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 0
## 2 Y 0
## Negative.fctr.predict.Final.rpart.Y
## 1 699
## 2 126
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Final.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 693 87
## Y 6 39
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 693
## 2 Y 87
## Negative.fctr.predict.Final.rpart.Y
## 1 6
## 2 39
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Final.rpart.Y
## 1 0
## 2 0
## Reference
## Prediction N Y
## N 699 126
## Y 0 0
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 699
## 2 Y 126
## Negative.fctr.predict.Final.rpart.Y
## 1 0
## 2 0
## threshold f.score
## 1 0.0 0.2649842
## 2 0.1 0.2649842
## 3 0.2 0.5000000
## 4 0.3 0.5000000
## 5 0.4 0.5000000
## 6 0.5 0.5000000
## 7 0.6 0.5000000
## 8 0.7 0.4561404
## 9 0.8 0.4561404
## 10 0.9 0.0000000
## 11 1.0 0.0000000
## [1] "Classifier Probability Threshold: 0.6000 to maximize f.score.fit"
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Reference
## Prediction N Y
## N 690 81
## Y 9 45
## Negative.fctr Negative.fctr.predict.Final.rpart.N
## 1 N 690
## 2 Y 81
## Negative.fctr.predict.Final.rpart.Y
## 1 9
## 2 45
## Prediction
## Reference N Y
## N 690 9
## Y 81 45
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 8.909091e-01 4.495596e-01 8.676171e-01 9.113614e-01 8.472727e-01
## AccuracyPValue McnemarPValue
## 1.744490e-04 7.206261e-14
## Warning in mypredict_mdl(mdl, df = fit_df, rsp_var, rsp_var_out,
## model_id_method, : Expecting 1 metric: Accuracy; recd: Accuracy, Kappa;
## retaining Accuracy only
## model_id model_method
## 1 Final.rpart rpart
## feats
## 1 freak, hate, wtf, stuff, fix, pictur, cant, better, shame, stupid, even, line, yooo, ever, charger, still, charg, disappoint, short, like, amazon, yall, break., imessag, stand, togeth, cheap, wont, make, carbon, darn, httpbitly18xc8dk, dear, facebook, X7evenstarz, condom, femal, money, theyr, batteri, china, turn, hope, life, sinc, steve, switch, everi, last, your, amaz, arent, date, divulg, ill, ive, lost, noth, worst, phone, care, way, year, updat, app, iphon, data, take, card, custom, die, event, problem, refus, two, use, screen, thing, buy, tri, cdp, doesnt, emiss, feel, fun, got, hour, macbook, miss, siri, start, upgrad, get, think, copi, guess, person, smart, best, differ, nsa, product, twitter, old, see, download, idea, job, mani, simpl, soon, team, technolog, wow, much, support, text, time, android, that, fingerprint, now, ask, colour, gonna, happen, man, give, hey, today, case, chang, one, work, day, nokia, preorder, look, awesom, done, featur, said, secur, seem, smartphon, thought, what, windowsphon, bit, realli, can, sure, dont, impress, call, didnt, drop, fail, first, improv, isnt, made, mean, put, yet, next., thank, market, blackberri, mac, never, plastic, right, sell, pleas, guy, scanner, want, black, free, innov, via, come, good, let, stop, color, compani, know, price, just, store, ios7, peopl, releas, say, anyon, devic, need, back, googl, itun, microsoft, will, appl, new, iphone5, ipad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.3 0.418
## max.auc.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.6727638 0.6 0.5 0.8812121
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.8676171 0.9113614 0.366133
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01376705 0.09747058
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed10 fit.data.training.all 6 0 313.567
## elapsed11 fit.data.training.all 6 1 319.780
glb_rsp_var_out <- paste0(glb_rsp_var_out, tail(names(glb_models_lst), 1))
# Used again in predict.data.new chunk
glb_get_predictions <- function(df) {
if (glb_is_regression) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, glb_rsp_var_out,
smooth=TRUE))
df[, paste0(glb_rsp_var_out, ".err")] <-
abs(df[, glb_rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
# incorporate glb_clf_proba_threshold
# shd it only be for glb_fin_mdl or for earlier models ?
if (glb_models_df[glb_models_df$model_id == glb_fin_mdl_id,
"opt.prob.threshold.fit"] !=
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id,
"opt.prob.threshold.fit"])
stop("user specification for probability threshold required")
else prob_threshold <-
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.OOB"]
df[, paste0(glb_rsp_var_out, ".prob")] <-
predict(glb_fin_mdl, newdata=df, type="prob")[, 2]
df[, glb_rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(glb_rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
}
return(df)
}
glb_trnent_df <- glb_get_predictions(df=glb_trnent_df)
print(glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
entity_df=glb_trnent_df))
## id cor.y exclude.as.feat cor.y.abs
## 96 freak 0.4220126658 0 0.4220126658
## 113 hate 0.2150466216 0 0.2150466216
## 304 wtf 0.1642996876 0 0.1642996876
## 249 stuff 0.1685214122 0 0.1685214122
## 94 fix 0.1076729878 0 0.1076729878
## 198 pictur 0.1642996876 0 0.1642996876
## 35 cant 0.1518891001 0 0.1518891001
## 23 better 0.1114365401 0 0.1114365401
## 6 amaz 0.0536765239 0 0.0536765239
## 7 amazon 0.0851663937 0 0.0851663937
## 8 android 0.0149137629 0 0.0149137629
## 10 anyon -0.0305482407 0 0.0305482407
## 11 app 0.0506802204 0 0.0506802204
## 12 appl -0.0533458207 0 0.0533458207
## 14 arent 0.0536765239 0 0.0536765239
## 15 ask 0.0102616884 0 0.0102616884
## 19 awesom 0.0033167112 0 0.0033167112
## 20 back -0.0405914861 0 0.0405914861
## 21 batteri 0.0626301156 0 0.0626301156
## 22 best 0.0267580922 0 0.0267580922
## 25 bit 0.0028697294 0 0.0028697294
## 26 black -0.0148064982 0 0.0148064982
## 27 blackberri -0.0076273067 0 0.0076273067
## 28 break. 0.0826293697 0 0.0826293697
## 32 buy 0.0380283000 0 0.0380283000
## 33 call -0.0025381970 0 0.0025381970
## 34 can 0.0017524556 0 0.0017524556
## 36 carbon 0.0709359262 0 0.0709359262
## 37 card 0.0429730405 0 0.0429730405
## 38 care 0.0527276703 0 0.0527276703
## 39 case 0.0086651648 0 0.0086651648
## 40 cdp 0.0341988646 0 0.0341988646
## 41 chang 0.0086651648 0 0.0086651648
## 42 charg 0.0970913594 0 0.0970913594
## 43 charger 0.1069355141 0 0.1069355141
## 44 cheap 0.0769587757 0 0.0769587757
## 45 china 0.0625002078 0 0.0625002078
## 46 color -0.0199770086 0 0.0199770086
## 47 colour 0.0102616884 0 0.0102616884
## 48 come -0.0172779254 0 0.0172779254
## 49 compani -0.0199770086 0 0.0199770086
## 51 condom 0.0673843716 0 0.0673843716
## 52 copi 0.0303310278 0 0.0303310278
## 55 custom 0.0429730405 0 0.0429730405
## 56 darn 0.0709359262 0 0.0709359262
## 57 data 0.0453496159 0 0.0453496159
## 58 date 0.0536765239 0 0.0536765239
## 59 day 0.0047078144 0 0.0047078144
## 60 dear 0.0681568527 0 0.0681568527
## 63 devic -0.0325564924 0 0.0325564924
## 64 didnt -0.0025381970 0 0.0025381970
## 65 die 0.0429730405 0 0.0429730405
## 66 differ 0.0267580922 0 0.0267580922
## 67 disappoint 0.0970913594 0 0.0970913594
## 69 divulg 0.0536765239 0 0.0536765239
## 70 doesnt 0.0341988646 0 0.0341988646
## 71 done 0.0033167112 0 0.0033167112
## 72 dont -0.0006481129 0 0.0006481129
## 73 download 0.0188746800 0 0.0188746800
## 74 drop -0.0025381970 0 0.0025381970
## 76 emiss 0.0341988646 0 0.0341988646
## 78 even 0.1158940633 0 0.1158940633
## 79 event 0.0429730405 0 0.0429730405
## 80 ever 0.1076729878 0 0.1076729878
## 81 everi 0.0564127109 0 0.0564127109
## 83 facebook 0.0681568527 0 0.0681568527
## 84 fail -0.0025381970 0 0.0025381970
## 85 featur 0.0033167112 0 0.0033167112
## 86 feel 0.0341988646 0 0.0341988646
## 87 femal 0.0673843716 0 0.0673843716
## 91 fingerprint 0.0129942941 0 0.0129942941
## 93 first -0.0025381970 0 0.0025381970
## 97 free -0.0162362822 0 0.0162362822
## 98 fun 0.0341988646 0 0.0341988646
## 101 get 0.0332332539 0 0.0332332539
## 102 give 0.0094009452 0 0.0094009452
## 104 gonna 0.0102616884 0 0.0102616884
## 105 good -0.0199770086 0 0.0199770086
## 106 googl -0.0405914861 0 0.0405914861
## 107 got 0.0341988646 0 0.0341988646
## 109 guess 0.0303310278 0 0.0303310278
## 110 guy -0.0109788835 0 0.0109788835
## 111 happen 0.0102616884 0 0.0102616884
## 115 hey 0.0094009452 0 0.0094009452
## 116 hope 0.0611434911 0 0.0611434911
## 117 hour 0.0341988646 0 0.0341988646
## 118 httpbitly18xc8dk 0.0709359262 0 0.0709359262
## 120 idea 0.0188746800 0 0.0188746800
## 121 ill 0.0536765239 0 0.0536765239
## 122 imessag 0.0826293697 0 0.0826293697
## 123 impress -0.0022363540 0 0.0022363540
## 124 improv -0.0025381970 0 0.0025381970
## 125 innov -0.0162362822 0 0.0162362822
## 128 ios7 -0.0266634900 0 0.0266634900
## 129 ipad -0.0646730918 0 0.0646730918
## 130 iphon 0.0473646206 0 0.0473646206
## 132 iphone5 -0.0621379328 0 0.0621379328
## 137 isnt -0.0025381970 0 0.0025381970
## 138 itun -0.0449613971 0 0.0449613971
## 139 ive 0.0536765239 0 0.0536765239
## 140 job 0.0188746800 0 0.0188746800
## 141 just -0.0237594870 0 0.0237594870
## 143 know -0.0199770086 0 0.0199770086
## 144 last 0.0549744869 0 0.0549744869
## 146 let -0.0199770086 0 0.0199770086
## 147 life 0.0611434911 0 0.0611434911
## 148 like 0.0962377709 0 0.0962377709
## 149 line 0.1158940633 0 0.1158940633
## 153 look 0.0035739823 0 0.0035739823
## 155 lost 0.0536765239 0 0.0536765239
## 157 mac -0.0076273067 0 0.0076273067
## 158 macbook 0.0341988646 0 0.0341988646
## 159 made -0.0025381970 0 0.0025381970
## 160 make 0.0727362480 0 0.0727362480
## 161 man 0.0102616884 0 0.0102616884
## 162 mani 0.0188746800 0 0.0188746800
## 163 market -0.0068841106 0 0.0068841106
## 165 mean -0.0025381970 0 0.0025381970
## 166 microsoft -0.0450714475 0 0.0450714475
## 168 miss 0.0341988646 0 0.0341988646
## 170 money 0.0673843716 0 0.0673843716
## 173 much 0.0183336696 0 0.0183336696
## 176 need -0.0357398233 0 0.0357398233
## 177 never -0.0076273067 0 0.0076273067
## 178 new -0.0536221452 0 0.0536221452
## 180 next. -0.0036050106 0 0.0036050106
## 182 nokia 0.0047078144 0 0.0047078144
## 183 noth 0.0536765239 0 0.0536765239
## 184 now 0.0129148701 0 0.0129148701
## 185 nsa 0.0267580922 0 0.0267580922
## 188 old 0.0202889470 0 0.0202889470
## 189 one 0.0070552161 0 0.0070552161
## 192 peopl -0.0266634900 0 0.0266634900
## 194 person 0.0303310278 0 0.0303310278
## 195 phone 0.0536044876 0 0.0536044876
## 199 plastic -0.0076273067 0 0.0076273067
## 201 pleas -0.0108398425 0 0.0108398425
## 203 preorder 0.0047078144 0 0.0047078144
## 204 price -0.0216753537 0 0.0216753537
## 207 problem 0.0429730405 0 0.0429730405
## 208 product 0.0267580922 0 0.0267580922
## 211 put -0.0025381970 0 0.0025381970
## 215 realli 0.0020955452 0 0.0020955452
## 217 refus 0.0429730405 0 0.0429730405
## 218 releas -0.0266634900 0 0.0266634900
## 219 right -0.0076273067 0 0.0076273067
## 220 said 0.0033167112 0 0.0033167112
## 223 say -0.0266634900 0 0.0266634900
## 224 scanner -0.0121497764 0 0.0121497764
## 225 screen 0.0387788990 0 0.0387788990
## 226 secur 0.0033167112 0 0.0033167112
## 227 see 0.0202889470 0 0.0202889470
## 228 seem 0.0033167112 0 0.0033167112
## 229 sell -0.0076273067 0 0.0076273067
## 232 shame 0.1405061949 0 0.1405061949
## 234 short 0.0970913594 0 0.0970913594
## 236 simpl 0.0188746800 0 0.0188746800
## 237 sinc 0.0611434911 0 0.0611434911
## 238 siri 0.0341988646 0 0.0341988646
## 239 smart 0.0303310278 0 0.0303310278
## 240 smartphon 0.0033167112 0 0.0033167112
## 242 soon 0.0188746800 0 0.0188746800
## 243 stand 0.0826293697 0 0.0826293697
## 244 start 0.0341988646 0 0.0341988646
## 245 steve 0.0611434911 0 0.0611434911
## 246 still 0.1007505594 0 0.1007505594
## 247 stop -0.0199770086 0 0.0199770086
## 248 store -0.0259113719 0 0.0259113719
## 250 stupid 0.1186461031 0 0.1186461031
## 252 support 0.0183336696 0 0.0183336696
## 253 sure 0.0003935570 0 0.0003935570
## 254 switch 0.0611434911 0 0.0611434911
## 255 take 0.0453496159 0 0.0453496159
## 257 team 0.0188746800 0 0.0188746800
## 259 technolog 0.0188746800 0 0.0188746800
## 261 text 0.0183336696 0 0.0183336696
## 262 thank -0.0049626450 0 0.0049626450
## 263 that 0.0145566668 0 0.0145566668
## 264 theyr 0.0673843716 0 0.0673843716
## 265 thing 0.0387788990 0 0.0387788990
## 266 think 0.0314859024 0 0.0314859024
## 268 thought 0.0033167112 0 0.0033167112
## 269 time 0.0159958708 0 0.0159958708
## 270 today 0.0094009452 0 0.0094009452
## 271 togeth 0.0826293697 0 0.0826293697
## 274 tri 0.0356343761 0 0.0356343761
## 276 turn 0.0625002078 0 0.0625002078
## 278 twitter 0.0207137058 0 0.0207137058
## 279 two 0.0429730405 0 0.0429730405
## 280 updat 0.0519176496 0 0.0519176496
## 281 upgrad 0.0341988646 0 0.0341988646
## 282 use 0.0401415689 0 0.0401415689
## 284 via -0.0162362822 0 0.0162362822
## 287 want -0.0141452654 0 0.0141452654
## 289 way 0.0527276703 0 0.0527276703
## 292 what 0.0033167112 0 0.0033167112
## 294 will -0.0463979478 0 0.0463979478
## 295 windowsphon 0.0033167112 0 0.0033167112
## 299 wont 0.0758516866 0 0.0758516866
## 300 work 0.0057872663 0 0.0057872663
## 302 worst 0.0536765239 0 0.0536765239
## 303 wow 0.0188746800 0 0.0188746800
## 306 X7evenstarz 0.0673843716 0 0.0673843716
## 307 yall 0.0851663937 0 0.0851663937
## 308 year 0.0527276703 0 0.0527276703
## 310 yet -0.0025381970 0 0.0025381970
## 311 yooo 0.1158940633 0 0.1158940633
## 312 your 0.0546349290 0 0.0546349290
## 1 .rnorm -0.0127552181 0 0.0127552181
## 2 actual -0.0346046348 0 0.0346046348
## 3 add -0.0256490570 0 0.0256490570
## 4 alreadi -0.0256490570 0 0.0256490570
## 5 alway -0.0296350116 0 0.0296350116
## 9 announc -0.0557827956 0 0.0557827956
## 13 appstor -0.0331531471 0 0.0331531471
## 16 avail -0.0420127055 0 0.0420127055
## 17 Avg -0.7061583707 1 0.7061583707
## 18 away -0.0331531471 0 0.0331531471
## 24 big -0.0392752586 0 0.0392752586
## 29 bring -0.0346046348 0 0.0346046348
## 30 burberri -0.0375405647 0 0.0375405647
## 31 busi -0.0420127055 0 0.0420127055
## 50 condescens -0.0296350116 0 0.0296350116
## 53 crack -0.0296350116 0 0.0296350116
## 54 creat -0.0331531471 0 0.0331531471
## 61 design -0.0331531471 0 0.0331531471
## 62 develop -0.0375405647 0 0.0375405647
## 68 discontinu -0.0296350116 0 0.0296350116
## 75 email -0.0296350116 0 0.0296350116
## 77 emoji -0.0392752586 0 0.0392752586
## 82 everyth -0.0209296403 0 0.0209296403
## 88 figur -0.0296350116 0 0.0296350116
## 89 final -0.0256490570 0 0.0256490570
## 90 finger -0.0445884997 0 0.0445884997
## 92 fire -0.0363396181 0 0.0363396181
## 95 follow -0.0420127055 0 0.0420127055
## 99 generat -0.0420127055 0 0.0420127055
## 100 genius -0.0256490570 0 0.0256490570
## 103 gold -0.0428877879 0 0.0428877879
## 108 great -0.0363396181 0 0.0363396181
## 112 happi -0.0331531471 0 0.0331531471
## 114 help -0.0470292313 0 0.0470292313
## 119 ibrooklynb -0.0331531471 0 0.0331531471
## 126 instead -0.0445884997 0 0.0445884997
## 127 internet -0.0296350116 0 0.0296350116
## 131 iphone4 -0.0296350116 0 0.0296350116
## 133 iphone5c -0.0645077026 0 0.0645077026
## 134 iphoto -0.0392752586 0 0.0392752586
## 135 ipod -0.0852874403 0 0.0852874403
## 136 ipodplayerpromo -0.0793460925 0 0.0793460925
## 142 keynot -0.0363396181 0 0.0363396181
## 145 launch -0.0331531471 0 0.0331531471
## 150 lmao -0.0296350116 0 0.0296350116
## 151 lock -0.0331531471 0 0.0331531471
## 152 lol -0.0537205371 0 0.0537205371
## 154 los -0.0296350116 0 0.0296350116
## 156 love -0.0577763105 0 0.0577763105
## 164 mayb -0.0363396181 0 0.0363396181
## 167 mishiza -0.0363396181 0 0.0363396181
## 169 mobil -0.0428877879 0 0.0428877879
## 171 motorola -0.0392752586 0 0.0392752586
## 172 move -0.0296350116 0 0.0296350116
## 174 music -0.0209296403 0 0.0209296403
## 175 natz0711 -0.0363396181 0 0.0363396181
## 179 news -0.0453528423 0 0.0453528423
## 181 nfc -0.0256490570 0 0.0256490570
## 186 nuevo -0.0296350116 0 0.0296350116
## 187 offer -0.0363396181 0 0.0363396181
## 190 page -0.0331531471 0 0.0331531471
## 191 para -0.0363396181 0 0.0363396181
## 193 perfect -0.0296350116 0 0.0296350116
## 196 photog -0.0296350116 0 0.0296350116
## 197 photographi -0.0296350116 0 0.0296350116
## 200 play -0.0331531471 0 0.0331531471
## 202 ppl -0.0223147692 0 0.0223147692
## 205 print -0.0470292313 0 0.0470292313
## 206 pro -0.0256490570 0 0.0256490570
## 209 promo -0.0615836556 0 0.0615836556
## 210 promoipodplayerpromo -0.0838913688 0 0.0838913688
## 212 que -0.0428877879 0 0.0428877879
## 213 quiet -0.0296350116 0 0.0296350116
## 214 read -0.0209296403 0 0.0209296403
## 216 recommend -0.0296350116 0 0.0296350116
## 221 samsung -0.0615836556 0 0.0615836556
## 222 samsungsa -0.0363396181 0 0.0363396181
## 230 send -0.0296350116 0 0.0296350116
## 231 servic -0.0470292313 0 0.0470292313
## 233 share -0.0346046348 0 0.0346046348
## 235 show -0.0363396181 0 0.0363396181
## 241 someth -0.0331531471 0 0.0331531471
## 251 suck 0.1644037549 0 0.1644037549
## 256 talk -0.0331531471 0 0.0331531471
## 258 tech -0.0470292313 0 0.0470292313
## 260 tell -0.0470292313 0 0.0470292313
## 267 tho -0.0296350116 0 0.0296350116
## 272 touch -0.0331531471 0 0.0331531471
## 273 touchid -0.0392752586 0 0.0392752586
## 275 true -0.0296350116 0 0.0296350116
## 277 Tweet.fctr 0.6354986940 1 0.6354986940
## 283 user -0.0296350116 0 0.0296350116
## 285 video -0.0375405647 0 0.0375405647
## 286 wait -0.0445884997 0 0.0445884997
## 288 watch -0.0363396181 0 0.0363396181
## 290 week -0.0375405647 0 0.0375405647
## 291 well -0.0506130885 0 0.0506130885
## 293 white -0.0346046348 0 0.0346046348
## 296 wish -0.0420127055 0 0.0420127055
## 297 without -0.0209296403 0 0.0209296403
## 298 wonder -0.0331531471 0 0.0331531471
## 301 world -0.0363396181 0 0.0363396181
## 305 X244tsuyoponzu -0.0331531471 0 0.0331531471
## 309 yes -0.0256490570 0 0.0256490570
## cor.high.X is.ConditionalX.y is.cor.y.abs.low importance
## 96 <NA> TRUE FALSE 100.000000
## 113 <NA> TRUE FALSE 48.971207
## 304 <NA> TRUE FALSE 44.063971
## 249 <NA> TRUE FALSE 43.568454
## 94 <NA> TRUE FALSE 14.928296
## 198 <NA> TRUE FALSE 14.675057
## 35 <NA> TRUE FALSE 14.195802
## 23 <NA> TRUE FALSE 7.296511
## 6 <NA> TRUE FALSE 0.000000
## 7 cdp TRUE FALSE 0.000000
## 8 <NA> TRUE FALSE 0.000000
## 10 <NA> TRUE FALSE 0.000000
## 11 <NA> TRUE FALSE 0.000000
## 12 <NA> TRUE FALSE 0.000000
## 14 <NA> TRUE FALSE 0.000000
## 15 <NA> TRUE TRUE 0.000000
## 19 <NA> TRUE TRUE 0.000000
## 20 <NA> TRUE FALSE 0.000000
## 21 <NA> TRUE FALSE 0.000000
## 22 <NA> TRUE FALSE 0.000000
## 25 <NA> TRUE TRUE 0.000000
## 26 <NA> TRUE FALSE 0.000000
## 27 <NA> TRUE TRUE 0.000000
## 28 <NA> TRUE FALSE 0.000000
## 32 <NA> TRUE FALSE 0.000000
## 33 <NA> TRUE TRUE 0.000000
## 34 <NA> TRUE TRUE 0.000000
## 36 httpbitly18xc8dk TRUE FALSE 0.000000
## 37 <NA> TRUE FALSE 0.000000
## 38 <NA> TRUE FALSE 0.000000
## 39 <NA> TRUE TRUE 0.000000
## 40 <NA> TRUE FALSE 0.000000
## 41 <NA> TRUE TRUE 0.000000
## 42 <NA> TRUE FALSE 0.000000
## 43 <NA> TRUE FALSE 0.000000
## 44 <NA> TRUE FALSE 0.000000
## 45 <NA> TRUE FALSE 0.000000
## 46 <NA> TRUE FALSE 0.000000
## 47 <NA> TRUE TRUE 0.000000
## 48 <NA> TRUE FALSE 0.000000
## 49 <NA> TRUE FALSE 0.000000
## 51 femal TRUE FALSE 0.000000
## 52 <NA> TRUE FALSE 0.000000
## 55 <NA> TRUE FALSE 0.000000
## 56 <NA> TRUE FALSE 0.000000
## 57 <NA> TRUE FALSE 0.000000
## 58 <NA> TRUE FALSE 0.000000
## 59 <NA> TRUE TRUE 0.000000
## 60 <NA> TRUE FALSE 0.000000
## 63 <NA> TRUE FALSE 0.000000
## 64 <NA> TRUE TRUE 0.000000
## 65 <NA> TRUE FALSE 0.000000
## 66 <NA> TRUE FALSE 0.000000
## 67 <NA> TRUE FALSE 0.000000
## 69 refus TRUE FALSE 0.000000
## 70 <NA> TRUE FALSE 0.000000
## 71 <NA> TRUE TRUE 0.000000
## 72 <NA> TRUE TRUE 0.000000
## 73 <NA> TRUE FALSE 0.000000
## 74 <NA> TRUE TRUE 0.000000
## 76 <NA> TRUE FALSE 0.000000
## 78 <NA> TRUE FALSE 0.000000
## 79 <NA> TRUE FALSE 0.000000
## 80 <NA> TRUE FALSE 0.000000
## 81 <NA> TRUE FALSE 0.000000
## 83 <NA> TRUE FALSE 0.000000
## 84 <NA> TRUE TRUE 0.000000
## 85 <NA> TRUE TRUE 0.000000
## 86 <NA> TRUE FALSE 0.000000
## 87 <NA> TRUE FALSE 0.000000
## 91 <NA> TRUE FALSE 0.000000
## 93 <NA> TRUE TRUE 0.000000
## 97 <NA> TRUE FALSE 0.000000
## 98 <NA> TRUE FALSE 0.000000
## 101 <NA> TRUE FALSE 0.000000
## 102 <NA> TRUE TRUE 0.000000
## 104 <NA> TRUE TRUE 0.000000
## 105 <NA> TRUE FALSE 0.000000
## 106 <NA> TRUE FALSE 0.000000
## 107 <NA> TRUE FALSE 0.000000
## 109 <NA> TRUE FALSE 0.000000
## 110 <NA> TRUE TRUE 0.000000
## 111 <NA> TRUE TRUE 0.000000
## 115 <NA> TRUE TRUE 0.000000
## 116 <NA> TRUE FALSE 0.000000
## 117 <NA> TRUE FALSE 0.000000
## 118 <NA> TRUE FALSE 0.000000
## 120 <NA> TRUE FALSE 0.000000
## 121 <NA> TRUE FALSE 0.000000
## 122 <NA> TRUE FALSE 0.000000
## 123 <NA> TRUE TRUE 0.000000
## 124 <NA> TRUE TRUE 0.000000
## 125 <NA> TRUE FALSE 0.000000
## 128 <NA> TRUE FALSE 0.000000
## 129 itun TRUE FALSE 0.000000
## 130 <NA> TRUE FALSE 0.000000
## 132 <NA> TRUE FALSE 0.000000
## 137 <NA> TRUE TRUE 0.000000
## 138 <NA> TRUE FALSE 0.000000
## 139 <NA> TRUE FALSE 0.000000
## 140 <NA> TRUE FALSE 0.000000
## 141 <NA> TRUE FALSE 0.000000
## 143 <NA> TRUE FALSE 0.000000
## 144 <NA> TRUE FALSE 0.000000
## 146 <NA> TRUE FALSE 0.000000
## 147 <NA> TRUE FALSE 0.000000
## 148 <NA> TRUE FALSE 0.000000
## 149 <NA> TRUE FALSE 0.000000
## 153 <NA> TRUE TRUE 0.000000
## 155 <NA> TRUE FALSE 0.000000
## 157 <NA> TRUE TRUE 0.000000
## 158 <NA> TRUE FALSE 0.000000
## 159 <NA> TRUE TRUE 0.000000
## 160 <NA> TRUE FALSE 0.000000
## 161 <NA> TRUE TRUE 0.000000
## 162 <NA> TRUE FALSE 0.000000
## 163 <NA> TRUE TRUE 0.000000
## 165 <NA> TRUE TRUE 0.000000
## 166 <NA> TRUE FALSE 0.000000
## 168 <NA> TRUE FALSE 0.000000
## 170 <NA> TRUE FALSE 0.000000
## 173 <NA> TRUE FALSE 0.000000
## 176 <NA> TRUE FALSE 0.000000
## 177 <NA> TRUE TRUE 0.000000
## 178 <NA> TRUE FALSE 0.000000
## 180 <NA> TRUE TRUE 0.000000
## 182 <NA> TRUE TRUE 0.000000
## 183 <NA> TRUE FALSE 0.000000
## 184 <NA> TRUE FALSE 0.000000
## 185 <NA> TRUE FALSE 0.000000
## 188 <NA> TRUE FALSE 0.000000
## 189 <NA> TRUE TRUE 0.000000
## 192 <NA> TRUE FALSE 0.000000
## 194 <NA> TRUE FALSE 0.000000
## 195 <NA> TRUE FALSE 0.000000
## 199 <NA> TRUE TRUE 0.000000
## 201 <NA> TRUE TRUE 0.000000
## 203 <NA> TRUE TRUE 0.000000
## 204 <NA> TRUE FALSE 0.000000
## 207 <NA> TRUE FALSE 0.000000
## 208 <NA> TRUE FALSE 0.000000
## 211 <NA> TRUE TRUE 0.000000
## 215 <NA> TRUE TRUE 0.000000
## 217 emiss TRUE FALSE 0.000000
## 218 <NA> TRUE FALSE 0.000000
## 219 <NA> TRUE TRUE 0.000000
## 220 <NA> TRUE TRUE 0.000000
## 223 <NA> TRUE FALSE 0.000000
## 224 <NA> TRUE TRUE 0.000000
## 225 <NA> TRUE FALSE 0.000000
## 226 <NA> TRUE TRUE 0.000000
## 227 <NA> TRUE FALSE 0.000000
## 228 <NA> TRUE TRUE 0.000000
## 229 <NA> TRUE TRUE 0.000000
## 232 <NA> TRUE FALSE 0.000000
## 234 <NA> TRUE FALSE 0.000000
## 236 <NA> TRUE FALSE 0.000000
## 237 <NA> TRUE FALSE 0.000000
## 238 <NA> TRUE FALSE 0.000000
## 239 <NA> TRUE FALSE 0.000000
## 240 <NA> TRUE TRUE 0.000000
## 242 <NA> TRUE FALSE 0.000000
## 243 <NA> TRUE FALSE 0.000000
## 244 <NA> TRUE FALSE 0.000000
## 245 <NA> TRUE FALSE 0.000000
## 246 <NA> TRUE FALSE 0.000000
## 247 <NA> TRUE FALSE 0.000000
## 248 <NA> TRUE FALSE 0.000000
## 250 <NA> TRUE FALSE 0.000000
## 252 <NA> TRUE FALSE 0.000000
## 253 <NA> TRUE TRUE 0.000000
## 254 <NA> TRUE FALSE 0.000000
## 255 <NA> TRUE FALSE 0.000000
## 257 <NA> TRUE FALSE 0.000000
## 259 <NA> TRUE FALSE 0.000000
## 261 <NA> TRUE FALSE 0.000000
## 262 <NA> TRUE TRUE 0.000000
## 263 <NA> TRUE FALSE 0.000000
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# Used again in predict.data.new chunk
glb_analytics_diag_plots <- function(obs_df) {
for (var in subset(glb_feats_df, importance > 0)$id) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, glb_rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
# plot_vars_df <- subset(glb_feats_df, importance >
# glb_feats_df[glb_feats_df$id == ".rnorm", "importance"])
plot_vars_df <- orderBy(~ -importance, glb_feats_df)
if (nrow(plot_vars_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(plot_vars_df <- subset(glb_feats_df, importance > 0)) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
glb_analytics_diag_plots(obs_df=glb_trnent_df)
## Tweet
## 1 I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore
## 4 Because @Apple has no idea how real people work, I have to erase my iphone & start over just for re-installing itunes after a new OS. #fun
## 5 Bc apple no longer impresses like it used to "@lmkunert: @Apple stock still on the decline after yesterday's iPhone Event. Not a good sign."
## 12 Okay so @apple apparently is making the new iPhone which the EXACT SAME THING as the old iPhone but now it has colour, I'm going to murder
## 16 why has it been 24 hours since i started restoring from icloud and my apps still arent downloaded??? @apple #darnyouiphone
## 17 .@tmcconnon You're the @Apple of my eye. Meaning you're a piece of crap.
## 18 Just spent a painful 20 minutes on the phone to iTunes support. Life's too short for this @apple.
## 38 @Apple I hate when they put out a new iPhone because then my iPhone feels old
## 200 WHY CANT I freakING SEE PICTURES ON MY TL IM ANNOYED freak YOU @TWITTER @APPLE
## 853 Lmao i hate u RT @JZFan: When are y'all gonna come out with the iJordans? @Apple @MichaelJordan
## 1152 tbh i always love black or gold jesus fjaskdghdka hate you @apple
## Avg
## 1 2.0
## 4 -1.0
## 5 -1.0
## 12 -1.0
## 16 -1.0
## 17 -1.0
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## 38 -1.0
## 200 -2.0
## 853 -0.2
## 1152 -0.8
## Tweet.fctr
## 1 I have to say, Apple has by far the best customer care service I have ever received! @Apple @AppStore
## 4 Because @Apple has no idea how real people work, I have to erase my iphone & start over just for re-installing itunes after a new OS. #fun
## 5 Bc apple no longer impresses like it used to "@lmkunert: @Apple stock still on the decline after yesterday's iPhone Event. Not a good sign."
## 12 Okay so @apple apparently is making the new iPhone which the EXACT SAME THING as the old iPhone but now it has colour, I'm going to murder
## 16 why has it been 24 hours since i started restoring from icloud and my apps still arent downloaded??? @apple #darnyouiphone
## 17 .@tmcconnon You're the @Apple of my eye. Meaning you're a piece of crap.
## 18 Just spent a painful 20 minutes on the phone to iTunes support. Life's too short for this @apple.
## 38 @Apple I hate when they put out a new iPhone because then my iPhone feels old
## 200 WHY CANT I freakING SEE PICTURES ON MY TL IM ANNOYED freak YOU @TWITTER @APPLE
## 853 Lmao i hate u RT @JZFan: When are y'all gonna come out with the iJordans? @Apple @MichaelJordan
## 1152 tbh i always love black or gold jesus fjaskdghdka hate you @apple
## .rnorm Negative.fctr X244tsuyoponzu X7evenstarz actual add
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## 1152 0 0 0 0 0 0 0 0 0 0 0
## wtf yall year yes yet yooo your
## 1 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 2
## 18 0 0 0 0 0 0 0
## 38 0 0 0 0 0 0 0
## 200 0 0 0 0 0 0 0
## 853 0 1 0 0 0 0 0
## 1152 0 0 0 0 0 0 0
## Negative.fctr.predict.Final.rpart.prob
## 1 0.1050584
## 4 0.1050584
## 5 0.1050584
## 12 0.1050584
## 16 0.1050584
## 17 0.1050584
## 18 0.1050584
## 38 0.8000000
## 200 0.8857143
## 853 0.8000000
## 1152 0.8000000
## Negative.fctr.predict.Final.rpart
## 1 N
## 4 N
## 5 N
## 12 N
## 16 N
## 17 N
## 18 N
## 38 Y
## 200 Y
## 853 Y
## 1152 Y
## Negative.fctr.predict.Final.rpart.accurate .label
## 1 TRUE .1
## 4 FALSE .4
## 5 FALSE .5
## 12 FALSE .12
## 16 FALSE .16
## 17 FALSE .17
## 18 FALSE .18
## 38 TRUE .38
## 200 TRUE .200
## 853 FALSE .853
## 1152 FALSE .1152
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="predict.data.new",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))
## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed11 fit.data.training.all 6 1 319.780
## elapsed12 predict.data.new 7 0 326.243
7: predict data.new# Compute final model predictions
glb_newent_df <- glb_get_predictions(glb_newent_df)
glb_analytics_diag_plots(obs_df=glb_newent_df)
## Tweet
## 6 i want ios7 freak can it come out already im getting real tired of waiting @apple
## 8 lol someone @apple is asleep at the wheel
## 9 iPhone battery was on 86% at 1am last night.. 53% when I woke up.. 20% an hour ago.. Now its on 2%. Cheers @Apple #Crapple
## 21 iOS 7 makes it 300% harder to change your wallpaper. Considering this is all the customization #iPhone users get, @Apple has some work to do
## 23 sorry @apple, without @stevejobs, you're just another company run by businessmen
## 28 I don't like #iOS7 they're changing the look of everything @apple
## 31 @riveraoor good ol terrible @apple
## 82 @Punk240z I just hate @Apple not the phone. You ignored your obligation to defend the female condom
## 151 Twitter and snapchat works but my iMessage always freaks up. What the freak @apple
## 666 @Microsoft Love what the design team has done with Metro but hate the recent jab @Apple.Why not just differentiate yourself from Apple?
## 997 @apple: love your products, hate inefficiencies of Genius Bar; why make an apt when I still need to wait 30mins?! http://4sq.com/1eOIu9d
## Avg
## 6 -1.0
## 8 -1.0
## 9 -1.0
## 21 -1.0
## 23 -1.0
## 28 -1.0
## 31 -1.0
## 82 -1.0
## 151 -1.4
## 666 0.0
## 997 -0.4
## Tweet.fctr
## 6 i want ios7 freak can it come out already im getting real tired of waiting @apple
## 8 lol someone @apple is asleep at the wheel
## 9 iPhone battery was on 86% at 1am last night.. 53% when I woke up.. 20% an hour ago.. Now its on 2%. Cheers @Apple #Crapple
## 21 iOS 7 makes it 300% harder to change your wallpaper. Considering this is all the customization #iPhone users get, @Apple has some work to do
## 23 sorry @apple, without @stevejobs, you're just another company run by businessmen
## 28 I don't like #iOS7 they're changing the look of everything @apple
## 31 @riveraoor good ol terrible @apple
## 82 @Punk240z I just hate @Apple not the phone. You ignored your obligation to defend the female condom
## 151 Twitter and snapchat works but my iMessage always freaks up. What the freak @apple
## 666 @Microsoft Love what the design team has done with Metro but hate the recent jab @Apple.Why not just differentiate yourself from Apple?
## 997 @apple: love your products, hate inefficiencies of Genius Bar; why make an apt when I still need to wait 30mins?! http://4sq.com/1eOIu9d
## .rnorm Negative.fctr X244tsuyoponzu X7evenstarz actual add alreadi
## 6 0.7260443 Y 0 0 0 0 1
## 8 -0.8152816 Y 0 0 0 0 0
## 9 0.1064385 Y 0 0 0 0 0
## 21 0.2173778 Y 0 0 0 0 0
## 23 0.4496361 Y 0 0 0 0 0
## 28 0.0258875 Y 0 0 0 0 0
## 31 0.1693495 Y 0 0 0 0 0
## 82 0.3176902 Y 0 0 0 0 0
## 151 0.1712761 Y 0 0 0 0 0
## 666 2.0957052 N 0 0 0 0 0
## 997 -0.2398467 N 0 0 0 0 0
## alway amaz amazon android announc anyon app appl appstor arent ask
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 1 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## avail away awesom back batteri best better big bit black blackberri
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
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## 31 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## break. bring burberri busi buy call can cant carbon card care case cdp
## 6 0 0 0 0 0 0 1 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0 0
## chang charg charger cheap china color colour come compani condescens
## 6 0 0 0 0 0 0 0 1 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 21 1 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 1 0
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## 31 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0
## condom copi crack creat custom darn data date day dear design develop
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 1 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0
## 82 1 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 1 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0
## devic didnt die differ disappoint discontinu divulg doesnt done dont
## 6 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 1
## 31 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 1 0
## 997 0 0 0 0 0 0 0 0 0 0
## download drop email emiss emoji even event ever everi everyth facebook
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 1 0
## 31 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## fail featur feel femal figur final finger fingerprint fire first fix
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 1 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## follow freak free fun generat genius get give gold gonna good googl
## 6 0 1 0 0 0 0 1 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 1 0 0 0 0 0
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## 28 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 1 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 2 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 1 0 0 0 0 0 0
## got great guess guy happen happi hate help hey hope hour
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 1
## 21 0 0 0 0 0 0 0 0 0 0 0
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## 28 0 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 0 0 0 1 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 1 0 0 0 0
## 997 0 0 0 0 0 0 1 0 0 0 0
## httpbitly18xc8dk ibrooklynb idea ill imessag impress improv innov
## 6 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0
## 151 0 0 0 0 1 0 0 0
## 666 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0
## instead internet ios7 ipad iphon iphone4 iphone5 iphone5c iphoto ipod
## 6 0 0 1 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 1 0 0 0 0 0
## 21 0 0 0 0 1 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0
## 28 0 0 1 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0
## ipodplayerpromo isnt itun ive job just keynot know last launch let
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 1 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 1 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 1 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 1 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## life like line lmao lock lol look los lost love mac macbook made make
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 1 0 0 0 0 1 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 1 0 0 0 1
## man mani market mayb mean microsoft mishiza miss mobil money motorola
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 1 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## move much music natz0711 need never new news next. nfc nokia noth now
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 1
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 1 0 0 0 0 0 0 0 0
## nsa nuevo offer old one page para peopl perfect person phone photog
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0 1 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0
## photographi pictur plastic play pleas ppl preorder price print pro
## 6 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0
## problem product promo promoipodplayerpromo put que quiet read realli
## 6 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0
## 31 0 0 0 0 0 0 0 0 0
## 82 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0
## 997 0 1 0 0 0 0 0 0 0
## recommend refus releas right said samsung samsungsa say scanner screen
## 6 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0
## secur see seem sell send servic shame share short show simpl sinc siri
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0 0 0
## 28 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0 0
## smart smartphon someth soon stand start steve still stop store stuff
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 1 0 0 0
## stupid suck support sure switch take talk team tech technolog tell
## 6 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
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## 21 0 0 0 0 0 0 0 0 0 0 0
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## 82 0 0 0 0 0 0 0 0 0 0 0
## 151 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 1 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## text thank that theyr thing think tho thought time today togeth touch
## 6 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0 0
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## 28 0 0 0 1 0 0 0 0 0 0 0 0
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## 151 0 0 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0
## touchid tri true turn twitter two updat upgrad use user via video wait
## 6 0 0 0 0 0 0 0 0 0 0 0 0 1
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0
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## 151 0 0 0 0 1 0 0 0 0 0 0 0 0
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## 997 0 0 0 0 0 0 0 0 0 0 0 0 1
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## 6 1 0 0 0 0 0 0 0 0 0 0
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## 666 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0
## wonder wont work world worst wow wtf yall year yes yet yooo your
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 1 0 0 0 0 0 0 0 0 0 0
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## 151 0 0 1 0 0 0 0 0 0 0 0 0 0
## 666 0 0 0 0 0 0 0 0 0 0 0 0 0
## 997 0 0 0 0 0 0 0 0 0 0 0 0 0
## Negative.fctr.predict.Final.rpart.prob
## 6 0.8857143
## 8 0.1050584
## 9 0.1050584
## 21 0.1050584
## 23 0.1050584
## 28 0.1050584
## 31 0.1050584
## 82 0.8000000
## 151 0.8857143
## 666 0.8000000
## 997 0.8000000
## Negative.fctr.predict.Final.rpart
## 6 Y
## 8 N
## 9 N
## 21 N
## 23 N
## 28 N
## 31 N
## 82 Y
## 151 Y
## 666 Y
## 997 Y
## Negative.fctr.predict.Final.rpart.accurate .label
## 6 TRUE .6
## 8 FALSE .8
## 9 FALSE .9
## 21 FALSE .21
## 23 FALSE .23
## 28 FALSE .28
## 31 FALSE .31
## 82 TRUE .82
## 151 TRUE .151
## 666 FALSE .666
## 997 FALSE .997
tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.new.prediction")), flip_coord=TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
## 6.0000 6 0 0 1 2
print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## chunk_label chunk_step_major chunk_step_minor elapsed
## 10 fit.models 5 1 299.425
## 11 fit.data.training.all 6 0 313.567
## 9 fit.models 5 0 24.668
## 7 select_features 4 0 14.222
## 13 predict.data.new 7 0 326.243
## 12 fit.data.training.all 6 1 319.780
## 6 extract_features 3 0 6.366
## 4 manage_missing_data 2 2 1.620
## 8 remove_correlated_features 4 1 15.261
## 5 encodeORretype.data 2 3 2.161
## 2 cleanse_data 2 0 0.503
## 3 inspectORexplore.data 2 1 0.538
## 1 import_data 1 0 0.002
## elapsed_diff
## 10 274.757
## 11 14.142
## 9 9.407
## 7 7.856
## 13 6.463
## 12 6.213
## 6 4.205
## 4 1.082
## 8 1.039
## 5 0.541
## 2 0.501
## 3 0.035
## 1 0.000
## [1] "Total Elapsed Time: 326.243 secs"
## R version 3.1.3 (2015-03-09)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tcltk grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] randomForest_4.6-10 rpart.plot_1.5.2 rpart_4.1-9
## [4] ROCR_1.0-7 gplots_2.16.0 caret_6.0-41
## [7] lattice_0.20-31 tm_0.6 NLP_0.1-6
## [10] sqldf_0.4-10 RSQLite_1.0.0 DBI_0.3.1
## [13] gsubfn_0.6-6 proto_0.3-10 reshape2_1.4.1
## [16] plyr_1.8.1 caTools_1.17.1 doBy_4.5-13
## [19] survival_2.38-1 ggplot2_1.0.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-6 BradleyTerry2_1.0-6 brglm_0.5-9
## [4] car_2.0-25 chron_2.3-45 class_7.3-12
## [7] codetools_0.2-11 colorspace_1.2-6 compiler_3.1.3
## [10] digest_0.6.8 e1071_1.6-4 evaluate_0.5.5
## [13] foreach_1.4.2 formatR_1.1 gdata_2.13.3
## [16] gtable_0.1.2 gtools_3.4.1 htmltools_0.2.6
## [19] iterators_1.0.7 KernSmooth_2.23-14 knitr_1.9
## [22] labeling_0.3 lme4_1.1-7 MASS_7.3-40
## [25] Matrix_1.2-0 mgcv_1.8-6 minqa_1.2.4
## [28] munsell_0.4.2 nlme_3.1-120 nloptr_1.0.4
## [31] nnet_7.3-9 parallel_3.1.3 pbkrtest_0.4-2
## [34] quantreg_5.11 RColorBrewer_1.1-2 Rcpp_0.11.5
## [37] rmarkdown_0.5.1 scales_0.2.4 slam_0.1-32
## [40] SparseM_1.6 splines_3.1.3 stringr_0.6.2
## [43] tools_3.1.3 yaml_2.1.13